{ "cells": [ { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "import os\n", "import numpy as np\n", "\n", "import pandas as pd\n", "import warnings\n", "from sklearn.linear_model import LinearRegression, HuberRegressor, Lasso, Ridge, ElasticNet\n", "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, AdaBoostRegressor\n", "from sklearn.neighbors import KNeighborsRegressor\n", "from sklearn.svm import SVR, LinearSVR\n", "from sklearn.tree import DecisionTreeRegressor\n", "from xgboost import XGBRegressor\n", "from lightgbm import LGBMRegressor\n", "import numpy as np\n", "from sklearn.metrics import mean_squared_error, r2_score\n", "from sklearn.svm import SVR\n", "from sklearn.model_selection import train_test_split, GridSearchCV\n", "from sklearn.metrics import mean_absolute_error, r2_score\n", "import matplotlib.pyplot as plt\n", "from sklearn.preprocessing import MinMaxScaler\n", "import seaborn as sns\n", "# Set random seed and display settings\n", "np.random.seed(42)\n", "plt.rcParams['figure.figsize'] = (16, 8)\n", "import logging\n", "from sklearn.exceptions import ConvergenceWarning # Import ConvergenceWarning\n", "\n", "warnings.filterwarnings(\"ignore\", category=ConvergenceWarning) # Suppress convergence warnings\n", "\n", "logging.getLogger(\"lightgbm\").setLevel(logging.ERROR)\n", "warnings.filterwarnings(\"ignore\", category=ConvergenceWarning)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# Load dataset\n", "file_path = os.path.join(os.getcwd(), \"data\", \"dynamic_pricing.csv\")\n", "data = pd.read_csv(file_path)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Number_of_RidersNumber_of_DriversLocation_CategoryCustomer_Loyalty_StatusNumber_of_Past_RidesAverage_RatingsTime_of_BookingVehicle_TypeExpected_Ride_DurationHistorical_Cost_of_Ride
09045UrbanSilver134.47NightPremium90284.257273
15839SuburbanSilver724.06EveningEconomy43173.874753
24231RuralSilver03.99AfternoonPremium76329.795469
38928RuralRegular674.31AfternoonPremium134470.201232
47822RuralRegular743.77AfternoonEconomy149579.681422
.................................
9953323UrbanGold244.21MorningPremium1191.389526
9968429UrbanRegular924.55MorningPremium94424.155987
997446SuburbanGold804.13NightPremium40157.364830
9985327SuburbanRegular783.63NightPremium58279.095048
9997863RuralGold144.21AfternoonEconomy147655.065106
\n", "

1000 rows × 10 columns

\n", "
" ], "text/plain": [ " Number_of_Riders Number_of_Drivers Location_Category \\\n", "0 90 45 Urban \n", "1 58 39 Suburban \n", "2 42 31 Rural \n", "3 89 28 Rural \n", "4 78 22 Rural \n", ".. ... ... ... \n", "995 33 23 Urban \n", "996 84 29 Urban \n", "997 44 6 Suburban \n", "998 53 27 Suburban \n", "999 78 63 Rural \n", "\n", " Customer_Loyalty_Status Number_of_Past_Rides Average_Ratings \\\n", "0 Silver 13 4.47 \n", "1 Silver 72 4.06 \n", "2 Silver 0 3.99 \n", "3 Regular 67 4.31 \n", "4 Regular 74 3.77 \n", ".. ... ... ... \n", "995 Gold 24 4.21 \n", "996 Regular 92 4.55 \n", "997 Gold 80 4.13 \n", "998 Regular 78 3.63 \n", "999 Gold 14 4.21 \n", "\n", " Time_of_Booking Vehicle_Type Expected_Ride_Duration \\\n", "0 Night Premium 90 \n", "1 Evening Economy 43 \n", "2 Afternoon Premium 76 \n", "3 Afternoon Premium 134 \n", "4 Afternoon Economy 149 \n", ".. ... ... ... \n", "995 Morning Premium 11 \n", "996 Morning Premium 94 \n", "997 Night Premium 40 \n", "998 Night Premium 58 \n", "999 Afternoon Economy 147 \n", "\n", " Historical_Cost_of_Ride \n", "0 284.257273 \n", "1 173.874753 \n", "2 329.795469 \n", "3 470.201232 \n", "4 579.681422 \n", ".. ... \n", "995 91.389526 \n", "996 424.155987 \n", "997 157.364830 \n", "998 279.095048 \n", "999 655.065106 \n", "\n", "[1000 rows x 10 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dataset shape: (1000, 10)\n", "Categorical columns: ['Location_Category', 'Customer_Loyalty_Status', 'Time_of_Booking', 'Vehicle_Type']\n" ] } ], "source": [ "# Initial data overview\n", "print(\"Dataset shape:\", data.shape)\n", "# Preprocess categorical columns\n", "categorical_columns = data.select_dtypes(include=['object']).columns\n", "print(\"Categorical columns:\", categorical_columns.tolist())\n", "\n", "# One-hot encode categorical variables\n", "data = pd.get_dummies(data, columns=categorical_columns, drop_first=True)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Categorical columns to convert: ['Location_Category_Suburban', 'Location_Category_Urban', 'Customer_Loyalty_Status_Regular', 'Customer_Loyalty_Status_Silver', 'Time_of_Booking_Evening', 'Time_of_Booking_Morning', 'Time_of_Booking_Night', 'Vehicle_Type_Premium']\n", "Number_of_Riders int64\n", "Number_of_Drivers int64\n", "Number_of_Past_Rides int64\n", "Average_Ratings float64\n", "Expected_Ride_Duration int64\n", "Historical_Cost_of_Ride float64\n", "Location_Category_Suburban int64\n", "Location_Category_Urban int64\n", "Customer_Loyalty_Status_Regular int64\n", "Customer_Loyalty_Status_Silver int64\n", "Time_of_Booking_Evening int64\n", "Time_of_Booking_Morning int64\n", "Time_of_Booking_Night int64\n", "Vehicle_Type_Premium int64\n", "dtype: object\n" ] } ], "source": [ "new_categorical_columns = [col for col in data.columns if data[col].dtype == 'bool']\n", "\n", "print(\"Categorical columns to convert:\", new_categorical_columns)\n", "# Convert only the categorical columns (boolean) to integers\n", "data[new_categorical_columns] = data[new_categorical_columns].astype(int)\n", "# Verify data types of the columns\n", "print(data.dtypes)\n", "\n", "# # Ensure values are 0 and 1\n", "# for col in new_categorical_columns:\n", "# print(f\"{col}: {data[col].unique()}\")\n", "\n", "data = data.sample(frac=1)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/linear_model/_huber.py:343: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/svm/_base.py:1243: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/svm/_base.py:1243: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/svm/_base.py:1243: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/svm/_base.py:1243: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/svm/_base.py:1243: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/svm/_base.py:1243: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/svm/_base.py:1243: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/svm/_base.py:1243: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/svm/_base.py:1243: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/svm/_base.py:1243: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/svm/_base.py:1243: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/svm/_base.py:1243: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/svm/_base.py:1243: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/svm/_base.py:1243: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/svm/_base.py:1243: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/svm/_base.py:1243: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/svm/_base.py:1243: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/svm/_base.py:1243: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/svm/_base.py:1243: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/svm/_base.py:1243: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/svm/_base.py:1243: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/svm/_base.py:1243: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/svm/_base.py:1243: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/svm/_base.py:1243: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/svm/_base.py:1243: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/svm/_base.py:1243: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/svm/_base.py:1243: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/svm/_base.py:1243: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/svm/_base.py:1243: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/svm/_base.py:1243: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The XGBRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Model XGBRegressor failed with error: 'super' object has no attribute '__sklearn_tags__'\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n", "/home/codespace/.local/lib/python3.12/site-packages/sklearn/utils/_tags.py:354: FutureWarning: The LGBMRegressor or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n", " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Model Mean_CV_MAE Test_MAE Test_MSE Test_R2\n", "0 LinearRegression 52.363491 50.849894 4263.014365 0.884841\n", "1 HuberRegressor 52.122650 50.748019 4274.583298 0.884528\n", "2 Lasso 51.889670 50.319286 4242.067139 0.885407\n", "3 Ridge 52.264829 50.805516 4264.353372 0.884805\n", "4 ElasticNet 52.175695 50.768140 4279.824363 0.884387\n", "5 RandomForestRegressor 52.475949 53.024551 4950.191104 0.866278\n", "6 GradientBoostingRegressor 52.995046 52.594410 4757.738505 0.871476\n", "7 AdaBoostRegressor 55.338850 55.691503 4833.876350 0.869420\n", "8 KNeighborsRegressor 57.779301 58.447883 5463.234888 0.852418\n", "9 SVR 52.150566 51.029759 4321.126742 0.883271\n", "10 LinearSVR 53.014461 51.699839 4446.987441 0.879871\n", "11 DecisionTreeRegressor 61.237990 66.839101 8046.306829 0.782640\n", "12 LGBMRegressor 53.794270 54.604495 5264.352100 0.857791\n", "\n", "All CV MAE for top model (LinearRegression):\n", "Params: {}, CV MAE: 52.363491101627574\n" ] } ], "source": [ "\n", "# Prepare data\n", "X = data.drop('Historical_Cost_of_Ride', axis=1)\n", "\n", "y = data['Historical_Cost_of_Ride']\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", "\n", "# Define models and parameters\n", "models_and_params = {\n", " \"LinearRegression\": (LinearRegression(fit_intercept=False), {}),\n", " \"HuberRegressor\": (HuberRegressor(fit_intercept=False), {\"alpha\": np.logspace(-2, 1, 5), \"epsilon\": [1.35, 1.5, 1.75]}),\n", " \"Lasso\": (Lasso(fit_intercept=False), {\"alpha\": np.logspace(-3, 0, 4)}),\n", " \"Ridge\": (Ridge(fit_intercept=False), {\"alpha\": np.logspace(-2, 1, 5), \"solver\": [\"auto\", \"svd\", \"cholesky\"]}),\n", " \"ElasticNet\": (ElasticNet(fit_intercept=False), {\"alpha\": np.logspace(-3, 0, 4), \"l1_ratio\": [0.1, 0.5, 0.9]}),\n", " \"RandomForestRegressor\": (\n", " RandomForestRegressor(),\n", " {\"n_estimators\": [50, 100, 200], \"max_depth\": [None, 10, 20], \"min_samples_split\": [2, 5, 10]}\n", " ),\n", " \"GradientBoostingRegressor\": (\n", " GradientBoostingRegressor(),\n", " {\"n_estimators\": [50, 100, 200], \"learning_rate\": [0.01, 0.1, 0.2], \"max_depth\": [3, 5, 7]}\n", " ),\n", " \"AdaBoostRegressor\": (\n", " AdaBoostRegressor(),\n", " {\"n_estimators\": [50, 100, 200], \"learning_rate\": [0.01, 0.1, 0.5]}\n", " ),\n", " \"KNeighborsRegressor\": (\n", " KNeighborsRegressor(),\n", " {\"n_neighbors\": [3, 5, 10], \"weights\": [\"uniform\", \"distance\"]}\n", " ),\n", " \"SVR\": (\n", " SVR(),\n", " {\"C\": np.logspace(-2, 2, 5), \"epsilon\": [0.1, 0.2, 0.5], \"kernel\": [\"linear\", \"rbf\"]}\n", " ),\n", " \"LinearSVR\": (\n", " LinearSVR(),\n", " {\"C\": np.logspace(-3, 2, 5), \"epsilon\": [0.1, 0.2]}\n", " ),\n", " \"DecisionTreeRegressor\": (\n", " DecisionTreeRegressor(),\n", " {\"max_depth\": [None, 10, 20, 30], \"min_samples_split\": [2, 5, 10]}\n", " ),\n", " \"XGBRegressor\": (\n", " XGBRegressor(),\n", " {\"n_estimators\": [50, 100, 200], \"learning_rate\": [0.01, 0.1, 0.2], \"max_depth\": [3, 5, 7]}\n", " ),\n", " \"LGBMRegressor\": (\n", " LGBMRegressor(verbose=-1),\n", " {\"n_estimators\": [50, 100, 200], \"learning_rate\": [0.01, 0.1, 0.2], \"num_leaves\": [31, 50, 100]}\n", " ),\n", "}\n", "\n", "# Re-evaluate models with data and include MAE for test data\n", "results = []\n", "for name, (model, params) in models_and_params.items():\n", " try:\n", " grid_search = GridSearchCV(model, params, scoring='neg_mean_absolute_error', cv=5, n_jobs=-1)\n", " grid_search.fit(X_train, y_train)\n", "\n", " best_model = grid_search.best_estimator_\n", " best_score = -grid_search.best_score_ # Convert to positive\n", " y_test_pred = best_model.predict(X_test)\n", "\n", " # Calculate metrics\n", " test_mse = mean_squared_error(y_test, y_test_pred)\n", " test_mae = mean_absolute_error(y_test, y_test_pred) # Test MAE\n", " test_r2 = r2_score(y_test, y_test_pred)\n", "\n", " # Extract CV MAE for all hyperparameter combinations\n", " cv_mae_all = -grid_search.cv_results_[\"mean_test_score\"] # Convert to positive MAE\n", " param_combinations = grid_search.cv_results_[\"params\"]\n", "\n", " results.append({\n", " \"Model\": name,\n", " \"Mean_CV_MAE\": best_score,\n", " \"Test_MAE\": test_mae,\n", " \"Test_MSE\": test_mse,\n", " \"Test_R2\": test_r2,\n", " \"Best_Params\": grid_search.best_params_,\n", " \"CV_MAE_All\": list(zip(param_combinations, cv_mae_all)), # Include all CV MAEs with hyperparameters\n", " \"Best_Model\": best_model\n", " })\n", " except Exception as e:\n", " print(f\"Model {name} failed with error: {e}\")\n", "\n", "# Convert scaled results to DataFrame\n", "results_df = pd.DataFrame(results)\n", "scaled_results_df = results_df.sort_values(by=\"Test_MAE\")\n", "\n", "# Display scaled results\n", "print(results_df[[\"Model\", \"Mean_CV_MAE\", \"Test_MAE\", \"Test_MSE\", \"Test_R2\"]])\n", "\n", "# Example: Access all CV MAE for the top model\n", "top_model_cv_mae = results_df.iloc[0][\"CV_MAE_All\"]\n", "print(f\"\\nAll CV MAE for top model ({results_df.iloc[0]['Model']}):\")\n", "for params, cv_mae in top_model_cv_mae:\n", " print(f\"Params: {params}, CV MAE: {cv_mae}\")" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best Model: Lasso\n", "Best Equation:\n", "y = -0.1398 * Number_of_Riders + 0.4665 * Number_of_Drivers + -0.0033 * Number_of_Past_Rides + 3.4973 * Expected_Ride_Duration + -2.4212 * Time_of_Booking_Evening + 39.5754 * Vehicle_Type_Premium + 0.0000\n", "Best Model: Lasso\n", "Best Parameters: {'alpha': np.float64(1.0)}\n" ] } ], "source": [ "# Extract the top-performing model\n", "top_model_row = scaled_results_df.iloc[0]\n", "top_model_name = top_model_row[\"Model\"]\n", "top_model = top_model_row[\"Best_Model\"]\n", "\n", "# Check if the model is linear (e.g., LinearRegression, Lasso, Ridge, ElasticNet, HuberRegressor)\n", "if hasattr(top_model, \"coef_\") and hasattr(top_model, \"intercept_\"):\n", " coefficients = top_model.coef_ # Coefficients of the features\n", " intercept = top_model.intercept_ # Intercept of the equation\n", "\n", " # Get feature names from the dataset\n", " feature_names = X_train.columns\n", "\n", " # Construct the equation, excluding terms with zero coefficients\n", " terms = [f\"{coef:.4f} * {feature}\" for coef, feature in zip(coefficients, feature_names) if coef != 0]\n", " equation = \"y = \" + \" + \".join(terms) + f\" + {intercept:.4f}\"\n", "\n", " print(f\"Best Model: {top_model_name}\")\n", " print(\"Best Equation:\")\n", " print(equation)\n", "\n", "else:\n", " print(f\"The best model '{top_model_name}' does not provide coefficients. It may not be a linear model.\")\n", "# Extract the top-performing model\n", "top_model_row = scaled_results_df.iloc[0]\n", "top_model_name = top_model_row[\"Model\"]\n", "top_model = top_model_row[\"Best_Model\"]\n", "top_model_params = top_model_row[\"Best_Params\"]\n", "\n", "# Print the best model details\n", "print(f\"Best Model: {top_model_name}\")\n", "print(f\"Best Parameters: {top_model_params}\")\n", "# Make predictions\n", "predictions = top_model.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Ensure the model has coefficients\n", "if hasattr(top_model, \"coef_\") and hasattr(top_model, \"intercept_\"):\n", " # Extract coefficients and feature names\n", " coefficients = top_model.coef_\n", " feature_names = X_train.columns\n", "\n", " # Sort coefficients by magnitude for better visualization\n", " sorted_indices = np.argsort(np.abs(coefficients))[::-1]\n", " sorted_coefficients = coefficients[sorted_indices]\n", " sorted_feature_names = feature_names[sorted_indices]\n", "\n", " # Plot coefficients\n", " plt.figure(figsize=(12, 8))\n", " colors = ['dodgerblue' if coef > 0 else 'crimson' for coef in sorted_coefficients]\n", "\n", " plt.barh(sorted_feature_names, sorted_coefficients, color=colors, edgecolor='black')\n", " plt.axvline(0, color=\"gray\", linestyle=\"--\", linewidth=1) # Add a line at 0 for reference\n", "\n", " # Add labels and title\n", " plt.xlabel(\"Coefficient Value\", fontsize=14, color=\"darkblue\")\n", " plt.ylabel(\"Feature\", fontsize=14, color=\"darkblue\")\n", " plt.title(f\"Feature Coefficients for {top_model_name}\", fontsize=16, fontweight=\"bold\", color=\"teal\")\n", "\n", " # Beautify ticks\n", " plt.xticks(fontsize=12, color=\"darkblue\")\n", " plt.yticks(fontsize=12, color=\"darkblue\")\n", "\n", " # Tight layout\n", " plt.tight_layout()\n", " plt.show()\n", "\n", "else:\n", " print(f\"The best model '{top_model_name}' does not provide coefficients. It may not be a linear model.\")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Enhanced Actual vs Predicted Values Plot\n", "plt.figure(figsize=(16, 8))\n", "\n", "# Scatter plot\n", "plt.scatter(y_test, predictions, alpha=0.7, edgecolor='k', label=\"Predictions\", color=\"dodgerblue\")\n", "\n", "# Perfect Prediction Line\n", "plt.plot(\n", " [y_test.min(), y_test.max()],\n", " [y_test.min(), y_test.max()],\n", " '--r',\n", " label=\"Perfect Prediction Line\",\n", " linewidth=2\n", ")\n", "\n", "# Additional aesthetics\n", "plt.xlabel(\"Actual Values\", fontsize=14, color=\"darkblue\")\n", "plt.ylabel(\"Predicted Values\", fontsize=14, color=\"darkblue\")\n", "plt.title(f\"Actual vs Predicted Values ({top_model_name})\", fontsize=16, fontweight=\"bold\", color=\"teal\")\n", "plt.legend(fontsize=12, loc=\"upper left\")\n", "plt.grid(alpha=0.3, linestyle='--', color='gray')\n", "plt.tight_layout()\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create a range for the x-axis\n", "x = np.arange(len(y_test))\n", "\n", "# Enhanced Bar Plot for Actual vs Predicted Values\n", "plt.figure(figsize=(16, 8))\n", "\n", "# Actual Values Bar\n", "plt.bar(\n", " x - 0.2,\n", " y_test,\n", " width=0.4,\n", " label='Actual Values',\n", " alpha=0.85,\n", " color='dodgerblue',\n", " edgecolor='k',\n", " linewidth=0.6\n", ")\n", "\n", "# Predicted Values Bar\n", "plt.bar(\n", " x + 0.2,\n", " predictions,\n", " width=0.4,\n", " label='Predictions',\n", " alpha=0.85,\n", " color='orange',\n", " edgecolor='k',\n", " linewidth=0.6\n", ")\n", "\n", "# Add Labels and Title\n", "plt.xlabel(\"Index\", fontsize=14, color=\"darkblue\")\n", "plt.ylabel(\"Values\", fontsize=14, color=\"darkblue\")\n", "plt.title(f\"Actual vs Predicted Values ({top_model_name})\", fontsize=16, fontweight=\"bold\", color=\"teal\")\n", "\n", "# Add Legend\n", "plt.legend(fontsize=12, loc=\"upper right\")\n", "\n", "# Add Grid\n", "plt.grid(axis='y', alpha=0.3, linestyle='--', color='gray')\n", "\n", "# Improve Layout\n", "plt.tight_layout()\n", "\n", "# Show Plot\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Define the number of bars to display\n", "sample_size = int(len(y_test)*0.2) # Adjust the sample size to reduce clutter\n", "\n", "# Create sampled indices for both actual and predicted values\n", "indices = np.linspace(0, len(y_test) - 1, sample_size, dtype=int)\n", "x = np.arange(len(indices))\n", "\n", "# Extract samples for plotting\n", "y_test_sample = np.array(y_test)[indices]\n", "predictions_sample = np.array(predictions)[indices]\n", "\n", "# Enhanced Bar Plot for Actual vs Predicted Values\n", "plt.figure(figsize=(12, 6))\n", "\n", "# Actual Values Bar\n", "plt.bar(\n", " x - 0.2,\n", " y_test_sample,\n", " width=0.4,\n", " label='Actual Values',\n", " alpha=0.9,\n", " color='skyblue',\n", " edgecolor='black'\n", ")\n", "\n", "# Predicted Values Bar\n", "plt.bar(\n", " x + 0.2,\n", " predictions_sample,\n", " width=0.4,\n", " label='Predictions',\n", " alpha=0.9,\n", " color='orange',\n", " edgecolor='black'\n", ")\n", "\n", "# Add Labels and Title\n", "plt.xlabel(\"Sample Index\", fontsize=14)\n", "plt.ylabel(\"Values\", fontsize=14)\n", "plt.title(f\"Actual vs Predicted Values ({top_model_name})\", fontsize=16, fontweight=\"bold\")\n", "\n", "# Customize x-axis ticks\n", "plt.xticks(x, indices, rotation=45, fontsize=10)\n", "\n", "# Add Legend\n", "plt.legend(fontsize=12, loc=\"best\")\n", "\n", "# Add Grid\n", "plt.grid(axis='y', alpha=0.4, linestyle='--')\n", "\n", "# Optimize Layout\n", "plt.tight_layout()\n", "\n", "# Show Plot\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Calculate residuals\n", "residuals = y_test - predictions\n", "\n", "# Pretty Residual Plot\n", "plt.figure(figsize=(14, 8))\n", "\n", "# Scatter plot for residuals\n", "plt.scatter(\n", " predictions,\n", " residuals,\n", " alpha=0.8,\n", " edgecolor='white',\n", " color='mediumvioletred',\n", " s=80, # Increase marker size\n", " label=\"Residuals\"\n", ")\n", "\n", "# Zero residual reference line\n", "plt.axhline(0, color='dodgerblue', linestyle='--', linewidth=2, label=\"Zero Residual Line\")\n", "\n", "# Labels and title\n", "plt.xlabel(\"Predicted Values\", fontsize=16, fontweight='bold', color=\"darkblue\")\n", "plt.ylabel(\"Residuals (Actual - Predicted)\", fontsize=16, fontweight='bold', color=\"darkblue\")\n", "plt.title(f\"Residual Plot ({top_model_name})\", fontsize=18, fontweight=\"bold\", color=\"teal\")\n", "\n", "# Legend\n", "plt.legend(fontsize=14, loc=\"upper right\", frameon=True, shadow=True, facecolor='white', edgecolor='gray')\n", "\n", "# Grid for clarity\n", "plt.grid(alpha=0.4, linestyle='--', color='gray')\n", "\n", "# Beautify axes\n", "plt.xticks(fontsize=12, color=\"darkblue\")\n", "plt.yticks(fontsize=12, color=\"darkblue\")\n", "\n", "# Improve layout\n", "plt.tight_layout()\n", "\n", "# Show plot\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Define the number of bars to display\n", "sample_size = 20 # Adjust the sample size to reduce clutter\n", "\n", "# Create sampled indices for both actual and predicted values\n", "indices = np.linspace(0, len(y_test) - 1, sample_size, dtype=int)\n", "x = np.arange(len(indices))\n", "\n", "# Extract samples for plotting\n", "y_test_sample = np.array(y_test)[indices]\n", "predictions_sample = np.array(predictions)[indices]\n", "\n", "# Enhanced Bar Plot for Actual vs Predicted Values\n", "plt.figure(figsize=(12, 6))\n", "\n", "# Actual Values Bar\n", "plt.bar(\n", " x - 0.2,\n", " y_test_sample,\n", " width=0.4,\n", " label='Actual Values',\n", " alpha=0.9,\n", " color='skyblue',\n", " edgecolor='black'\n", ")\n", "\n", "# Predicted Values Bar\n", "plt.bar(\n", " x + 0.2,\n", " predictions_sample,\n", " width=0.4,\n", " label='Predictions',\n", " alpha=0.9,\n", " color='orange',\n", " edgecolor='black'\n", ")\n", "\n", "# Add Labels and Title\n", "plt.xlabel(\"Sample Index\", fontsize=14)\n", "plt.ylabel(\"Values\", fontsize=14)\n", "plt.title(f\"Actual vs Predicted Values ({top_model_name})\", fontsize=16, fontweight=\"bold\")\n", "\n", "# Customize x-axis ticks\n", "plt.xticks(x, indices, rotation=45, fontsize=10)\n", "\n", "# Add Legend\n", "plt.legend(fontsize=12, loc=\"best\")\n", "\n", "# Add Grid\n", "plt.grid(axis='y', alpha=0.4, linestyle='--')\n", "\n", "# Optimize Layout\n", "plt.tight_layout()\n", "\n", "# Show Plot\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(residuals, bins=30, color=\"purple\", edgecolor=\"white\")\n", "plt.title(\"Distribution of Residuals\")\n", "plt.xlabel(\"Residuals\")\n", "plt.ylabel(\"Frequency\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot residuals with KDE for confidence\n", "sns.residplot(x=predictions, y=residuals, lowess=True, color=\"purple\", scatter_kws={'alpha':0.5})\n", "plt.axhline(0, color='dodgerblue', linestyle='--', linewidth=2)\n", "plt.title(\"Residual Plot with Confidence Band\")\n", "plt.xlabel(\"Predicted Values\")\n", "plt.ylabel(\"Residuals\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.1" } }, "nbformat": 4, "nbformat_minor": 2 }