{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a6e704f6",
   "metadata": {},
   "source": [
    "# A5b: Tree based regression models\n",
    "\n",
    "challenge the A5 champion (R²=0.7204) with tree-based regression\n",
    "\n",
    "A5 champion: weighted averaging ensemble (Lasso, Ridge, RF, GradientBoosting)\n",
    "\n",
    "A5b Focus:\n",
    "- Decision Tree Regression with different depths and pruning\n",
    "- AdaBoost Regression with DecisionTree base estimators\n",
    "- Enhanced Tree Boosting (XGBoost, LightGBM with tuning)\n",
    "- Hyperparameter Optimization using RandomizedSearchCV\n",
    "- Statistical significance testing (Nadeau-Bengio corrected t-test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8815ff52",
   "metadata": {},
   "source": [
    "## 1. Setup & Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42785f6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup complete.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import pickle\n",
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from pathlib import Path\n",
    "from scipy import stats\n",
    "\n",
    "from sklearn.tree import DecisionTreeRegressor, plot_tree\n",
    "from sklearn.ensemble import (\n",
    "    AdaBoostRegressor, \n",
    "    GradientBoostingRegressor,\n",
    "    RandomForestRegressor,\n",
    "    ExtraTreesRegressor\n",
    ")\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import (\n",
    "    cross_val_score, KFold, RandomizedSearchCV, train_test_split\n",
    ")\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "\n",
    "import xgboost as xgb\n",
    "import lightgbm as lgb\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "np.random.seed(42)\n",
    "\n",
    "sys.path.insert(0, os.path.join(os.getcwd(), '..'))\n",
    "sys.path.insert(0, os.path.join(os.getcwd(), '..', 'A5'))\n",
    "\n",
    "RANDOM_STATE = 42\n",
    "N_SPLITS = 5\n",
    "CHAMPION_R2 = 0.7204 #a5\n",
    "\n",
    "OUT_DIR = Path('models')\n",
    "OUT_DIR.mkdir(exist_ok=True)\n",
    "\n",
    "print(\"Setup complete.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7078bf91",
   "metadata": {},
   "source": [
    "## 2. Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ce15290",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original train: (1675, 43)\n",
      "Bootstrap samples: 4 sets\n",
      "Test set: (419, 43)\n"
     ]
    }
   ],
   "source": [
    "DATA_DIR = os.path.join(os.getcwd(), '..', 'A5', 'data')\n",
    "\n",
    "df_train_original = pd.read_csv(os.path.join(DATA_DIR, \"dataset2_train_original.csv\"))\n",
    "\n",
    "df_train_aug = {}\n",
    "for i in range(1, 5):\n",
    "    df_train_aug[i] = pd.read_csv(os.path.join(DATA_DIR, f\"dataset2_train_augmented_{i}.csv\"))\n",
    "\n",
    "df_test = pd.read_csv(os.path.join(DATA_DIR, \"dataset2_test.csv\"))\n",
    "\n",
    "print(f\"Original train: {df_train_original.shape}\")\n",
    "print(f\"Bootstrap samples: {len(df_train_aug)} sets\")\n",
    "print(f\"Test set: {df_test.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90a376ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features: 40\n",
      "Train samples: 1675\n",
      "Test samples: 419\n"
     ]
    }
   ],
   "source": [
    "TARGET = \"AimoScore\"\n",
    "DROP_COLS = [\"ID\", \"EstimatedScore\", TARGET]\n",
    "\n",
    "feature_columns = [c for c in df_train_original.columns if c not in DROP_COLS]\n",
    "\n",
    "X_train = df_train_original[feature_columns].values\n",
    "y_train = df_train_original[TARGET].values\n",
    "X_test = df_test[feature_columns].values\n",
    "y_test = df_test[TARGET].values\n",
    "\n",
    "print(f\"Features: {len(feature_columns)}\")\n",
    "print(f\"Train samples: {X_train.shape[0]}\")\n",
    "print(f\"Test samples: {X_test.shape[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e1bcedb",
   "metadata": {},
   "source": [
    "## 3. Evaluation Utilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f7d47a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation utilities defined.\n"
     ]
    }
   ],
   "source": [
    "cv_strategy = KFold(n_splits=N_SPLITS, shuffle=True, random_state=RANDOM_STATE)\n",
    "\n",
    "def evaluate_model(model, X, y, cv, name='Model'):\n",
    "    r2_scores = cross_val_score(model, X, y, cv=cv, scoring='r2')\n",
    "    neg_rmse_scores = cross_val_score(model, X, y, cv=cv, scoring='neg_root_mean_squared_error')\n",
    "    neg_mae_scores = cross_val_score(model, X, y, cv=cv, scoring='neg_mean_absolute_error')\n",
    "    \n",
    "    return {\n",
    "        'Model': name,\n",
    "        'R2_mean': r2_scores.mean(),\n",
    "        'R2_std': r2_scores.std(),\n",
    "        'RMSE_mean': -neg_rmse_scores.mean(),\n",
    "        'MAE_mean': -neg_mae_scores.mean(),\n",
    "        '_r2_scores': r2_scores  # For t-test\n",
    "    }\n",
    "\n",
    "def evaluate_test(model, X_test, y_test)\n",
    "    y_pred = model.predict(X_test)\n",
    "    return {\n",
    "        'R2': r2_score(y_test, y_pred),\n",
    "        'RMSE': np.sqrt(mean_squared_error(y_test, y_pred)),\n",
    "        'MAE': mean_absolute_error(y_test, y_pred),\n",
    "        'Corr': np.corrcoef(y_test, y_pred)[0, 1]\n",
    "    }\n",
    "\n",
    "def corrected_resampled_ttest(scores_a, scores_b, n_train, n_test):\n",
    "    k = len(scores_a)\n",
    "    diff = scores_a - scores_b\n",
    "    d_bar = diff.mean()\n",
    "    s_sq = diff.var(ddof=1)\n",
    "    var_corr = (1/k + n_test/n_train) * s_sq\n",
    "    if var_corr == 0:\n",
    "        return 0.0, 1.0\n",
    "    t_stat = d_bar / np.sqrt(var_corr)\n",
    "    p_value = 2 * (1 - stats.t.cdf(abs(t_stat), df=k-1))\n",
    "    return float(t_stat), float(p_value)\n",
    "\n",
    "print(\"Evaluation utilities defined.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86c9d7f0",
   "metadata": {},
   "source": [
    "## 4. Decision Tree Regression\n",
    "\n",
    "Train DecisionTreeRegressor with different depths and pruning strategies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f085acb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree - Varying max_depth\n",
      "Depth      CV R²        CV RMSE      Test R²     \n",
      "--------------------------------------------------\n",
      "3          0.4931       0.1663       0.5435      \n",
      "5          0.5276       0.1605       0.5924      \n",
      "10         0.4432       0.1740       0.5262      \n",
      "15         0.3915       0.1820       0.4733      \n",
      "20         0.4033       0.1803       0.4539      \n",
      "None       0.3966       0.1812       0.4491      \n",
      "\n",
      "Best depth: 5 (CV R²: 0.5276)\n"
     ]
    }
   ],
   "source": [
    "# Test different max_depth values\n",
    "depth_results = []\n",
    "\n",
    "depths = [3, 5, 10, 15, 20, None]\n",
    "\n",
    "print(\"Decision Tree - Varying max_depth\")\n",
    "print(f\"{'Depth':<10} {'CV R²':<12} {'CV RMSE':<12} {'Test R²':<12}\")\n",
    "print(\"-\" * 50)\n",
    "\n",
    "for depth in depths:\n",
    "    dt = DecisionTreeRegressor(\n",
    "        max_depth=depth,\n",
    "        min_samples_split=5,\n",
    "        min_samples_leaf=2,\n",
    "        random_state=RANDOM_STATE\n",
    "    )\n",
    "    \n",
    "    cv_res = evaluate_model(dt, X_train, y_train, cv_strategy, f'DT_depth_{depth}')\n",
    "    dt.fit(X_train, y_train)\n",
    "    test_res = evaluate_test(dt, X_test, y_test)\n",
    "    \n",
    "    depth_results.append({\n",
    "        'depth': depth,\n",
    "        'cv_r2': cv_res['R2_mean'],\n",
    "        'cv_rmse': cv_res['RMSE_mean'],\n",
    "        'test_r2': test_res['R2'],\n",
    "        '_r2_scores': cv_res['_r2_scores']\n",
    "    })\n",
    "    \n",
    "    depth_str = str(depth) if depth else 'None'\n",
    "    print(f\"{depth_str:<10} {cv_res['R2_mean']:<12.4f} {cv_res['RMSE_mean']:<12.4f} {test_res['R2']:<12.4f}\")\n",
    "\n",
    "# Find best depth\n",
    "best_depth_idx = np.argmax([r['cv_r2'] for r in depth_results])\n",
    "best_depth = depth_results[best_depth_idx]['depth']\n",
    "print(f\"\\nBest depth: {best_depth} (CV R²: {depth_results[best_depth_idx]['cv_r2']:.4f})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7c7de519",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize depth vs performance\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
    "\n",
    "depth_labels = [str(d) if d else 'None' for d in depths]\n",
    "cv_r2s = [r['cv_r2'] for r in depth_results]\n",
    "test_r2s = [r['test_r2'] for r in depth_results]\n",
    "\n",
    "axes[0].bar(depth_labels, cv_r2s, color='steelblue', edgecolor='black')\n",
    "axes[0].axhline(CHAMPION_R2, color='red', linestyle='--', label=f'A5 Champion R²={CHAMPION_R2}')\n",
    "axes[0].set_xlabel('Max Depth')\n",
    "axes[0].set_ylabel('CV R²')\n",
    "axes[0].set_title('Decision Tree: CV R² by Depth')\n",
    "axes[0].legend()\n",
    "\n",
    "axes[1].bar(depth_labels, test_r2s, color='orange', edgecolor='black')\n",
    "axes[1].axhline(CHAMPION_R2, color='red', linestyle='--', label=f'A5 Champion R²={CHAMPION_R2}')\n",
    "axes[1].set_xlabel('Max Depth')\n",
    "axes[1].set_ylabel('Test R²')\n",
    "axes[1].set_title('Decision Tree: Test R² by Depth')\n",
    "axes[1].legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33d1ac8e",
   "metadata": {},
   "source": [
    "### 4.1 Cost Complexity Pruning (ccp_alpha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f02be3b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cost-Complexity Pruning Results\n",
      "ccp_alpha    CV R²        Test R²      n_leaves    \n",
      "--------------------------------------------------\n",
      "0.000000     0.3849       0.4544       1641        \n",
      "0.000000     0.3849       0.4544       1461        \n",
      "0.000000     0.3851       0.4547       1281        \n",
      "0.000000     0.3853       0.4548       1102        \n",
      "0.000000     0.3858       0.4554       924         \n",
      "0.000001     0.3856       0.4536       747         \n",
      "0.000002     0.3847       0.4563       566         \n",
      "0.000006     0.3969       0.4573       387         \n",
      "0.000023     0.4254       0.4977       202         \n",
      "0.011719     0.1067       -0.0026      1           \n"
     ]
    }
   ],
   "source": [
    "# Get cost-complexity pruning path\n",
    "dt_full = DecisionTreeRegressor(random_state=RANDOM_STATE)\n",
    "dt_full.fit(X_train, y_train)\n",
    "\n",
    "path = dt_full.cost_complexity_pruning_path(X_train, y_train)\n",
    "ccp_alphas = path.ccp_alphas\n",
    "\n",
    "# Test a subset of ccp_alpha values\n",
    "alpha_indices = np.linspace(0, len(ccp_alphas)-1, 10, dtype=int)\n",
    "test_alphas = ccp_alphas[alpha_indices]\n",
    "\n",
    "pruning_results = []\n",
    "\n",
    "print(\"\\nCost-Complexity Pruning Results\")\n",
    "print(f\"{'ccp_alpha':<12} {'CV R²':<12} {'Test R²':<12} {'n_leaves':<12}\")\n",
    "print(\"-\" * 50)\n",
    "\n",
    "for alpha in test_alphas:\n",
    "    dt_pruned = DecisionTreeRegressor(\n",
    "        ccp_alpha=alpha,\n",
    "        random_state=RANDOM_STATE\n",
    "    )\n",
    "    \n",
    "    cv_res = evaluate_model(dt_pruned, X_train, y_train, cv_strategy, f'DT_alpha_{alpha:.4f}')\n",
    "    dt_pruned.fit(X_train, y_train)\n",
    "    test_res = evaluate_test(dt_pruned, X_test, y_test)\n",
    "    \n",
    "    pruning_results.append({\n",
    "        'alpha': alpha,\n",
    "        'cv_r2': cv_res['R2_mean'],\n",
    "        'test_r2': test_res['R2'],\n",
    "        'n_leaves': dt_pruned.get_n_leaves(),\n",
    "        '_r2_scores': cv_res['_r2_scores']\n",
    "    })\n",
    "    \n",
    "    print(f\"{alpha:<12.6f} {cv_res['R2_mean']:<12.4f} {test_res['R2']:<12.4f} {dt_pruned.get_n_leaves():<12}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22b276a7",
   "metadata": {},
   "source": [
    "### 4.2 Best Decision Tree & Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bb2fdc92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Decision Tree (depth=5)\n",
      "CV R²: 0.5276 +/- 0.0441\n",
      "Test R²: 0.5924\n",
      "Test RMSE: 0.1560\n"
     ]
    }
   ],
   "source": [
    "# Train best decision tree\n",
    "best_dt = DecisionTreeRegressor(\n",
    "    max_depth=best_depth if best_depth else 15,\n",
    "    min_samples_split=5,\n",
    "    min_samples_leaf=2,\n",
    "    random_state=RANDOM_STATE\n",
    ")\n",
    "\n",
    "dt_cv = evaluate_model(best_dt, X_train, y_train, cv_strategy, 'Best Decision Tree')\n",
    "best_dt.fit(X_train, y_train)\n",
    "dt_test = evaluate_test(best_dt, X_test, y_test)\n",
    "\n",
    "print(f\"Best Decision Tree (depth={best_depth})\")\n",
    "print(f\"CV R²: {dt_cv['R2_mean']:.4f} +/- {dt_cv['R2_std']:.4f}\")\n",
    "print(f\"Test R²: {dt_test['R2']:.4f}\")\n",
    "print(f\"Test RMSE: {dt_test['RMSE']:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "24d4cca9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Top 10 Features:\n",
      "              feature  importance\n",
      " No_12_NASM_Deviation    0.360188\n",
      " No_10_NASM_Deviation    0.311209\n",
      " No_17_NASM_Deviation    0.129138\n",
      "  No_3_NASM_Deviation    0.032821\n",
      "  No_9_NASM_Deviation    0.024440\n",
      "  No_8_NASM_Deviation    0.022999\n",
      "No_11_Angle_Deviation    0.021838\n",
      "  No_7_NASM_Deviation    0.017870\n",
      "  No_1_Time_Deviation    0.017502\n",
      " No_22_NASM_Deviation    0.014611\n"
     ]
    }
   ],
   "source": [
    "# Feature importance from Decision Tree\n",
    "dt_importance = pd.DataFrame({\n",
    "    'feature': feature_columns,\n",
    "    'importance': best_dt.feature_importances_\n",
    "}).sort_values('importance', ascending=False)\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "top_n = 20\n",
    "plt.barh(dt_importance['feature'][:top_n][::-1], \n",
    "         dt_importance['importance'][:top_n][::-1],\n",
    "         color='forestgreen', edgecolor='black')\n",
    "plt.xlabel('Feature Importance')\n",
    "plt.title(f'Decision Tree: Top {top_n} Feature Importances')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"\\nTop 10 Features:\")\n",
    "print(dt_importance.head(10).to_string(index=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2e1a895",
   "metadata": {},
   "source": [
    "## 5. AdaBoost Regression\n",
    "\n",
    "Implement AdaBoostRegressor with DecisionTree base estimators."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "14a71998",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoost Regression Experiments\n",
      "n_est    lr       loss         CV R²        Test R²     \n",
      "-------------------------------------------------------\n",
      "50       0.1      linear       0.6709       0.7005      \n",
      "100      0.1      linear       0.6756       0.7096      \n",
      "200      0.05     linear       0.6743       0.7213      \n",
      "100      0.1      square       0.6722       0.7099      \n",
      "100      0.1      exponential  0.6731       0.7119      \n",
      "150      0.08     linear       0.6725       0.7108      \n",
      "\n",
      "Best AdaBoost: n_est=100, lr=0.1, loss=linear\n",
      "CV R²: 0.6756, Test R²: 0.7096\n"
     ]
    }
   ],
   "source": [
    "# AdaBoost with different configurations\n",
    "adaboost_configs = [\n",
    "    {'n_estimators': 50, 'learning_rate': 0.1, 'loss': 'linear'},\n",
    "    {'n_estimators': 100, 'learning_rate': 0.1, 'loss': 'linear'},\n",
    "    {'n_estimators': 200, 'learning_rate': 0.05, 'loss': 'linear'},\n",
    "    {'n_estimators': 100, 'learning_rate': 0.1, 'loss': 'square'},\n",
    "    {'n_estimators': 100, 'learning_rate': 0.1, 'loss': 'exponential'},\n",
    "    {'n_estimators': 150, 'learning_rate': 0.08, 'loss': 'linear'},\n",
    "]\n",
    "\n",
    "adaboost_results = []\n",
    "\n",
    "print(\"AdaBoost Regression Experiments\")\n",
    "print(f\"{'n_est':<8} {'lr':<8} {'loss':<12} {'CV R²':<12} {'Test R²':<12}\")\n",
    "print(\"-\" * 55)\n",
    "\n",
    "for config in adaboost_configs:\n",
    "    base_estimator = DecisionTreeRegressor(\n",
    "        max_depth=5,\n",
    "        min_samples_split=5,\n",
    "        random_state=RANDOM_STATE\n",
    "    )\n",
    "    \n",
    "    ada = AdaBoostRegressor(\n",
    "        estimator=base_estimator,\n",
    "        n_estimators=config['n_estimators'],\n",
    "        learning_rate=config['learning_rate'],\n",
    "        loss=config['loss'],\n",
    "        random_state=RANDOM_STATE\n",
    "    )\n",
    "    \n",
    "    cv_res = evaluate_model(ada, X_train, y_train, cv_strategy, f\"Ada_{config['n_estimators']}\")\n",
    "    ada.fit(X_train, y_train)\n",
    "    test_res = evaluate_test(ada, X_test, y_test)\n",
    "    \n",
    "    adaboost_results.append({\n",
    "        'config': config,\n",
    "        'cv_r2': cv_res['R2_mean'],\n",
    "        'cv_r2_std': cv_res['R2_std'],\n",
    "        'test_r2': test_res['R2'],\n",
    "        'model': ada,\n",
    "        '_r2_scores': cv_res['_r2_scores']\n",
    "    })\n",
    "    \n",
    "    print(f\"{config['n_estimators']:<8} {config['learning_rate']:<8} {config['loss']:<12} {cv_res['R2_mean']:<12.4f} {test_res['R2']:<12.4f}\")\n",
    "\n",
    "# Best AdaBoost\n",
    "best_ada_idx = np.argmax([r['cv_r2'] for r in adaboost_results])\n",
    "best_ada = adaboost_results[best_ada_idx]\n",
    "print(f\"\\nBest AdaBoost: n_est={best_ada['config']['n_estimators']}, lr={best_ada['config']['learning_rate']}, loss={best_ada['config']['loss']}\")\n",
    "print(f\"CV R²: {best_ada['cv_r2']:.4f}, Test R²: {best_ada['test_r2']:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fea2a77",
   "metadata": {},
   "source": [
    "## 6. Enhanced Tree Boosting\n",
    "\n",
    "Tune GradientBoosting, XGBoost, and LightGBM with regularization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ce3640cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost Regression Experiments\n",
      "n_est    depth    lr       alpha    lambda   CV R²        Test R²     \n",
      "---------------------------------------------------------------------------\n",
      "200      5        0.1      0        1        0.6981       0.7474      \n",
      "200      5        0.1      0.1      1        0.7022       0.7526      \n",
      "200      6        0.08     0.05     1.5      0.7100       0.7650      \n",
      "300      4        0.05     0.1      2        0.7073       0.7576      \n",
      "\n",
      "Best XGBoost CV R²: 0.7100, Test R²: 0.7650\n"
     ]
    }
   ],
   "source": [
    "# XGBoost with regularization\n",
    "xgb_configs = [\n",
    "    {'n_estimators': 200, 'max_depth': 5, 'learning_rate': 0.1, 'reg_alpha': 0, 'reg_lambda': 1},\n",
    "    {'n_estimators': 200, 'max_depth': 5, 'learning_rate': 0.1, 'reg_alpha': 0.1, 'reg_lambda': 1},\n",
    "    {'n_estimators': 200, 'max_depth': 6, 'learning_rate': 0.08, 'reg_alpha': 0.05, 'reg_lambda': 1.5},\n",
    "    {'n_estimators': 300, 'max_depth': 4, 'learning_rate': 0.05, 'reg_alpha': 0.1, 'reg_lambda': 2},\n",
    "]\n",
    "\n",
    "xgb_results = []\n",
    "\n",
    "print(\"XGBoost Regression Experiments\")\n",
    "print(f\"{'n_est':<8} {'depth':<8} {'lr':<8} {'alpha':<8} {'lambda':<8} {'CV R²':<12} {'Test R²':<12}\")\n",
    "print(\"-\" * 75)\n",
    "\n",
    "for config in xgb_configs:\n",
    "    xgb_model = xgb.XGBRegressor(\n",
    "        n_estimators=config['n_estimators'],\n",
    "        max_depth=config['max_depth'],\n",
    "        learning_rate=config['learning_rate'],\n",
    "        reg_alpha=config['reg_alpha'],\n",
    "        reg_lambda=config['reg_lambda'],\n",
    "        subsample=0.8,\n",
    "        colsample_bytree=0.8,\n",
    "        random_state=RANDOM_STATE,\n",
    "        n_jobs=-1,\n",
    "        verbosity=0\n",
    "    )\n",
    "    \n",
    "    cv_res = evaluate_model(xgb_model, X_train, y_train, cv_strategy, 'XGBoost')\n",
    "    xgb_model.fit(X_train, y_train)\n",
    "    test_res = evaluate_test(xgb_model, X_test, y_test)\n",
    "    \n",
    "    xgb_results.append({\n",
    "        'config': config,\n",
    "        'cv_r2': cv_res['R2_mean'],\n",
    "        'test_r2': test_res['R2'],\n",
    "        'model': xgb_model,\n",
    "        '_r2_scores': cv_res['_r2_scores']\n",
    "    })\n",
    "    \n",
    "    print(f\"{config['n_estimators']:<8} {config['max_depth']:<8} {config['learning_rate']:<8} {config['reg_alpha']:<8} {config['reg_lambda']:<8} {cv_res['R2_mean']:<12.4f} {test_res['R2']:<12.4f}\")\n",
    "\n",
    "best_xgb_idx = np.argmax([r['cv_r2'] for r in xgb_results])\n",
    "best_xgb = xgb_results[best_xgb_idx]\n",
    "print(f\"\\nBest XGBoost CV R²: {best_xgb['cv_r2']:.4f}, Test R²: {best_xgb['test_r2']:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f47ced26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "LightGBM Regression Experiments\n",
      "n_est    leaves   lr       child    CV R²        Test R²     \n",
      "------------------------------------------------------------\n",
      "200      31       0.1      20       0.6996       0.7542      \n",
      "200      50       0.08     15       0.7080       0.7677      \n",
      "300      40       0.05     10       0.7138       0.7619      \n",
      "\n",
      "Best LightGBM CV R²: 0.7138, Test R²: 0.7619\n"
     ]
    }
   ],
   "source": [
    "# LightGBM\n",
    "lgb_configs = [\n",
    "    {'n_estimators': 200, 'num_leaves': 31, 'learning_rate': 0.1, 'min_child_samples': 20},\n",
    "    {'n_estimators': 200, 'num_leaves': 50, 'learning_rate': 0.08, 'min_child_samples': 15},\n",
    "    {'n_estimators': 300, 'num_leaves': 40, 'learning_rate': 0.05, 'min_child_samples': 10},\n",
    "]\n",
    "\n",
    "lgb_results = []\n",
    "\n",
    "print(\"\\nLightGBM Regression Experiments\")\n",
    "print(f\"{'n_est':<8} {'leaves':<8} {'lr':<8} {'child':<8} {'CV R²':<12} {'Test R²':<12}\")\n",
    "print(\"-\" * 60)\n",
    "\n",
    "for config in lgb_configs:\n",
    "    lgb_model = lgb.LGBMRegressor(\n",
    "        n_estimators=config['n_estimators'],\n",
    "        num_leaves=config['num_leaves'],\n",
    "        learning_rate=config['learning_rate'],\n",
    "        min_child_samples=config['min_child_samples'],\n",
    "        subsample=0.8,\n",
    "        colsample_bytree=0.8,\n",
    "        random_state=RANDOM_STATE,\n",
    "        n_jobs=-1,\n",
    "        verbosity=-1\n",
    "    )\n",
    "    \n",
    "    cv_res = evaluate_model(lgb_model, X_train, y_train, cv_strategy, 'LightGBM')\n",
    "    lgb_model.fit(X_train, y_train)\n",
    "    test_res = evaluate_test(lgb_model, X_test, y_test)\n",
    "    \n",
    "    lgb_results.append({\n",
    "        'config': config,\n",
    "        'cv_r2': cv_res['R2_mean'],\n",
    "        'test_r2': test_res['R2'],\n",
    "        'model': lgb_model,\n",
    "        '_r2_scores': cv_res['_r2_scores']\n",
    "    })\n",
    "    \n",
    "    print(f\"{config['n_estimators']:<8} {config['num_leaves']:<8} {config['learning_rate']:<8} {config['min_child_samples']:<8} {cv_res['R2_mean']:<12.4f} {test_res['R2']:<12.4f}\")\n",
    "\n",
    "best_lgb_idx = np.argmax([r['cv_r2'] for r in lgb_results])\n",
    "best_lgb = lgb_results[best_lgb_idx]\n",
    "print(f\"\\nBest LightGBM CV R²: {best_lgb['cv_r2']:.4f}, Test R²: {best_lgb['test_r2']:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95271f91",
   "metadata": {},
   "source": [
    "## 7. Hyperparameter Optimization with RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "be4ed44d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running RandomizedSearchCV for XGBoost...\n",
      "Fitting 5 folds for each of 30 candidates, totalling 150 fits\n",
      "\n",
      "Best XGBoost Parameters:\n",
      "{'colsample_bytree': np.float64(0.695824756266789), 'learning_rate': np.float64(0.03897897441824462), 'max_depth': 6, 'n_estimators': 317, 'reg_alpha': np.float64(0.49282522705530035), 'reg_lambda': np.float64(0.9841105430230008), 'subsample': np.float64(0.8688542189623514)}\n",
      "Best CV R²: 0.7119\n",
      "Test R²: 0.7614\n"
     ]
    }
   ],
   "source": [
    "# RandomizedSearchCV for XGBoost\n",
    "from scipy.stats import uniform, randint\n",
    "\n",
    "xgb_param_dist = {\n",
    "    'n_estimators': randint(100, 400),\n",
    "    'max_depth': randint(3, 10),\n",
    "    'learning_rate': uniform(0.01, 0.2),\n",
    "    'subsample': uniform(0.6, 0.4),\n",
    "    'colsample_bytree': uniform(0.6, 0.4),\n",
    "    'reg_alpha': uniform(0, 0.5),\n",
    "    'reg_lambda': uniform(0.5, 2),\n",
    "}\n",
    "\n",
    "xgb_base = xgb.XGBRegressor(\n",
    "    random_state=RANDOM_STATE,\n",
    "    n_jobs=-1,\n",
    "    verbosity=0\n",
    ")\n",
    "\n",
    "print(\"Running RandomizedSearchCV for XGBoost...\")\n",
    "xgb_search = RandomizedSearchCV(\n",
    "    xgb_base,\n",
    "    xgb_param_dist,\n",
    "    n_iter=30,\n",
    "    cv=cv_strategy,\n",
    "    scoring='r2',\n",
    "    random_state=RANDOM_STATE,\n",
    "    n_jobs=-1,\n",
    "    verbose=1\n",
    ")\n",
    "\n",
    "xgb_search.fit(X_train, y_train)\n",
    "\n",
    "print(f\"\\nBest XGBoost Parameters:\")\n",
    "print(xgb_search.best_params_)\n",
    "print(f\"Best CV R²: {xgb_search.best_score_:.4f}\")\n",
    "\n",
    "# Evaluate on test\n",
    "xgb_optimized = xgb_search.best_estimator_\n",
    "xgb_opt_test = evaluate_test(xgb_optimized, X_test, y_test)\n",
    "print(f\"Test R²: {xgb_opt_test['R2']:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19c6cae5",
   "metadata": {},
   "source": [
    "## 8. Extra Trees Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "41ab3fe9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExtraTrees Regression Experiments\n",
      "n_est      max_depth    CV R²        Test R²     \n",
      "--------------------------------------------------\n",
      "100        15           0.7032       0.7427      \n",
      "200        20           0.7031       0.7443      \n",
      "200        None         0.7038       0.7456      \n",
      "\n",
      "Best ExtraTrees CV R²: 0.7038, Test R²: 0.7456\n"
     ]
    }
   ],
   "source": [
    "# ExtraTrees (Extremely Randomized Trees)\n",
    "et_configs = [\n",
    "    {'n_estimators': 100, 'max_depth': 15},\n",
    "    {'n_estimators': 200, 'max_depth': 20},\n",
    "    {'n_estimators': 200, 'max_depth': None},\n",
    "]\n",
    "\n",
    "et_results = []\n",
    "\n",
    "print(\"ExtraTrees Regression Experiments\")\n",
    "print(f\"{'n_est':<10} {'max_depth':<12} {'CV R²':<12} {'Test R²':<12}\")\n",
    "print(\"-\" * 50)\n",
    "\n",
    "for config in et_configs:\n",
    "    et = ExtraTreesRegressor(\n",
    "        n_estimators=config['n_estimators'],\n",
    "        max_depth=config['max_depth'],\n",
    "        min_samples_split=5,\n",
    "        min_samples_leaf=2,\n",
    "        random_state=RANDOM_STATE,\n",
    "        n_jobs=-1\n",
    "    )\n",
    "    \n",
    "    cv_res = evaluate_model(et, X_train, y_train, cv_strategy, 'ExtraTrees')\n",
    "    et.fit(X_train, y_train)\n",
    "    test_res = evaluate_test(et, X_test, y_test)\n",
    "    \n",
    "    et_results.append({\n",
    "        'config': config,\n",
    "        'cv_r2': cv_res['R2_mean'],\n",
    "        'test_r2': test_res['R2'],\n",
    "        'model': et,\n",
    "        '_r2_scores': cv_res['_r2_scores']\n",
    "    })\n",
    "    \n",
    "    depth_str = str(config['max_depth']) if config['max_depth'] else 'None'\n",
    "    print(f\"{config['n_estimators']:<10} {depth_str:<12} {cv_res['R2_mean']:<12.4f} {test_res['R2']:<12.4f}\")\n",
    "\n",
    "best_et_idx = np.argmax([r['cv_r2'] for r in et_results])\n",
    "best_et = et_results[best_et_idx]\n",
    "print(f\"\\nBest ExtraTrees CV R²: {best_et['cv_r2']:.4f}, Test R²: {best_et['test_r2']:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "621f1c66",
   "metadata": {},
   "source": [
    "## 9. Results Summary & Statistical Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "52c351dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======================================================================\n",
      "A5b TREE-BASED REGRESSION - RESULTS SUMMARY\n",
      "======================================================================\n",
      "A5 Champion (Weighted Ensemble): R² = 0.7204\n",
      "\n",
      "              Model    CV_R2  Test_R2  vs_Champion\n",
      "           LightGBM 0.713814 0.761890     0.041490\n",
      "XGBoost (optimized) 0.711885 0.761446     0.041046\n",
      "    XGBoost (tuned) 0.710045 0.764981     0.044581\n",
      "         ExtraTrees 0.703815 0.745570     0.025170\n",
      "           AdaBoost 0.675591 0.709623    -0.010777\n",
      "      Decision Tree 0.527570 0.592415    -0.127985\n"
     ]
    }
   ],
   "source": [
    "# Collect all best models\n",
    "all_models = [\n",
    "    {'name': 'Decision Tree', 'cv_r2': dt_cv['R2_mean'], 'test_r2': dt_test['R2'], '_r2_scores': dt_cv['_r2_scores'], 'model': best_dt},\n",
    "    {'name': 'AdaBoost', 'cv_r2': best_ada['cv_r2'], 'test_r2': best_ada['test_r2'], '_r2_scores': best_ada['_r2_scores'], 'model': best_ada['model']},\n",
    "    {'name': 'XGBoost (tuned)', 'cv_r2': best_xgb['cv_r2'], 'test_r2': best_xgb['test_r2'], '_r2_scores': best_xgb['_r2_scores'], 'model': best_xgb['model']},\n",
    "    {'name': 'XGBoost (optimized)', 'cv_r2': xgb_search.best_score_, 'test_r2': xgb_opt_test['R2'], '_r2_scores': None, 'model': xgb_optimized},\n",
    "    {'name': 'LightGBM', 'cv_r2': best_lgb['cv_r2'], 'test_r2': best_lgb['test_r2'], '_r2_scores': best_lgb['_r2_scores'], 'model': best_lgb['model']},\n",
    "    {'name': 'ExtraTrees', 'cv_r2': best_et['cv_r2'], 'test_r2': best_et['test_r2'], '_r2_scores': best_et['_r2_scores'], 'model': best_et['model']},\n",
    "]\n",
    "\n",
    "# Create summary DataFrame\n",
    "summary_df = pd.DataFrame([{\n",
    "    'Model': m['name'],\n",
    "    'CV_R2': m['cv_r2'],\n",
    "    'Test_R2': m['test_r2'],\n",
    "    'vs_Champion': m['test_r2'] - CHAMPION_R2\n",
    "} for m in all_models])\n",
    "\n",
    "summary_df = summary_df.sort_values('CV_R2', ascending=False).reset_index(drop=True)\n",
    "\n",
    "print(\"=\" * 70)\n",
    "print(\"A5b TREE-BASED REGRESSION - RESULTS SUMMARY\")\n",
    "print(\"=\" * 70)\n",
    "print(f\"A5 Champion (Weighted Ensemble): R² = {CHAMPION_R2}\")\n",
    "print(\"\\n\" + summary_df.to_string(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "81c55a8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "STATISTICAL SIGNIFICANCE TESTS vs A5 Champion\n",
      "------------------------------------------------------------\n",
      "Decision Tree             t=-5.825  p=0.0043  (Significant)\n",
      "AdaBoost                  t=-2.755  p=0.0511  (Not significant)\n",
      "XGBoost (tuned)           t=-0.643  p=0.5550  (Not significant)\n",
      "LightGBM                  t=-0.402  p=0.7083  (Not significant)\n",
      "ExtraTrees                t=-0.946  p=0.3977  (Not significant)\n"
     ]
    }
   ],
   "source": [
    "# Create a fake champion score array for t-test comparison\n",
    "# Using best model scores as reference\n",
    "n_total = len(X_train)\n",
    "n_test_fold = n_total // N_SPLITS\n",
    "n_train_fold = n_total - n_test_fold\n",
    "\n",
    "# Create champion reference scores (simulated from champion R²)\n",
    "champion_scores = np.array([CHAMPION_R2] * N_SPLITS)\n",
    "\n",
    "print(\"\\nSTATISTICAL SIGNIFICANCE TESTS vs A5 Champion\")\n",
    "print(\"-\" * 60)\n",
    "\n",
    "for m in all_models:\n",
    "    if m['_r2_scores'] is not None:\n",
    "        t, p = corrected_resampled_ttest(\n",
    "            m['_r2_scores'], champion_scores, n_train_fold, n_test_fold\n",
    "        )\n",
    "        sig = 'Significant' if p < 0.05 else 'Not significant'\n",
    "        print(f\"{m['name']:<25} t={t:+.3f}  p={p:.4f}  ({sig})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "169a0363",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "models = summary_df['Model'].tolist()\n",
    "cv_r2 = summary_df['CV_R2'].tolist()\n",
    "test_r2 = summary_df['Test_R2'].tolist()\n",
    "\n",
    "x = np.arange(len(models))\n",
    "width = 0.35\n",
    "\n",
    "bars1 = ax.bar(x - width/2, cv_r2, width, label='CV R²', color='steelblue', edgecolor='black')\n",
    "bars2 = ax.bar(x + width/2, test_r2, width, label='Test R²', color='orange', edgecolor='black')\n",
    "\n",
    "ax.axhline(CHAMPION_R2, color='red', linestyle='--', linewidth=2, label=f'A5 Champion R²={CHAMPION_R2}')\n",
    "\n",
    "ax.set_xlabel('Model')\n",
    "ax.set_ylabel('R² Score')\n",
    "ax.set_title('A5b Tree-Based Regression: Model Comparison')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(models, rotation=30, ha='right')\n",
    "ax.legend()\n",
    "ax.set_ylim([0.4, 0.85])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73f8fd93",
   "metadata": {},
   "source": [
    "## 10. Feature Importance Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b87e6f1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare feature importance across tree models\n",
    "fig, axes = plt.subplots(2, 2, figsize=(14, 12))\n",
    "\n",
    "top_n = 15\n",
    "\n",
    "# Decision Tree\n",
    "dt_imp = pd.DataFrame({'feature': feature_columns, 'importance': best_dt.feature_importances_})\n",
    "dt_imp = dt_imp.nlargest(top_n, 'importance')\n",
    "axes[0, 0].barh(dt_imp['feature'][::-1], dt_imp['importance'][::-1], color='forestgreen')\n",
    "axes[0, 0].set_title('Decision Tree')\n",
    "\n",
    "# AdaBoost\n",
    "ada_imp = pd.DataFrame({'feature': feature_columns, 'importance': best_ada['model'].feature_importances_})\n",
    "ada_imp = ada_imp.nlargest(top_n, 'importance')\n",
    "axes[0, 1].barh(ada_imp['feature'][::-1], ada_imp['importance'][::-1], color='coral')\n",
    "axes[0, 1].set_title('AdaBoost')\n",
    "\n",
    "# XGBoost\n",
    "xgb_imp = pd.DataFrame({'feature': feature_columns, 'importance': xgb_optimized.feature_importances_})\n",
    "xgb_imp = xgb_imp.nlargest(top_n, 'importance')\n",
    "axes[1, 0].barh(xgb_imp['feature'][::-1], xgb_imp['importance'][::-1], color='royalblue')\n",
    "axes[1, 0].set_title('XGBoost (optimized)')\n",
    "\n",
    "# LightGBM\n",
    "lgb_imp = pd.DataFrame({'feature': feature_columns, 'importance': best_lgb['model'].feature_importances_})\n",
    "lgb_imp = lgb_imp.nlargest(top_n, 'importance')\n",
    "axes[1, 1].barh(lgb_imp['feature'][::-1], lgb_imp['importance'][::-1], color='purple')\n",
    "axes[1, 1].set_title('LightGBM')\n",
    "\n",
    "for ax in axes.flat:\n",
    "    ax.set_xlabel('Feature Importance')\n",
    "\n",
    "plt.suptitle(f'Top {top_n} Feature Importances by Model', fontsize=14, fontweight='bold')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7009b940",
   "metadata": {},
   "source": [
    "## 11. Save Best Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "29b63acf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model: LightGBM\n",
      "CV R²: 0.7138\n",
      "Test R²: 0.7619\n",
      "A5 Champion R²: 0.7204\n",
      "Improvement: +0.0415\n",
      "\n",
      "✓ New model beats A5 champion! Saving...\n",
      "Saved to: models/regression_tree_champion.pkl\n"
     ]
    }
   ],
   "source": [
    "# Find overall best model\n",
    "best_model_idx = summary_df['CV_R2'].idxmax()\n",
    "best_model_name = summary_df.loc[best_model_idx, 'Model']\n",
    "best_model_cv_r2 = summary_df.loc[best_model_idx, 'CV_R2']\n",
    "best_model_test_r2 = summary_df.loc[best_model_idx, 'Test_R2']\n",
    "\n",
    "# Get the actual model object\n",
    "best_model_obj = next(m['model'] for m in all_models if m['name'] == best_model_name)\n",
    "\n",
    "print(f\"Best Model: {best_model_name}\")\n",
    "print(f\"CV R²: {best_model_cv_r2:.4f}\")\n",
    "print(f\"Test R²: {best_model_test_r2:.4f}\")\n",
    "print(f\"A5 Champion R²: {CHAMPION_R2}\")\n",
    "print(f\"Improvement: {best_model_test_r2 - CHAMPION_R2:+.4f}\")\n",
    "\n",
    "if best_model_test_r2 > CHAMPION_R2:\n",
    "    print(\"\\n✓ New model beats A5 champion! Saving...\")\n",
    "    \n",
    "    artifact = {\n",
    "        'model': best_model_obj,\n",
    "        'model_name': best_model_name,\n",
    "        'feature_columns': feature_columns,\n",
    "        'cv_r2': best_model_cv_r2,\n",
    "        'test_r2': best_model_test_r2,\n",
    "        'champion_r2': CHAMPION_R2,\n",
    "        'improvement': best_model_test_r2 - CHAMPION_R2\n",
    "    }\n",
    "    \n",
    "    out_path = OUT_DIR / 'regression_tree_champion.pkl'\n",
    "    with open(out_path, 'wb') as f:\n",
    "        pickle.dump(artifact, f)\n",
    "    \n",
    "    print(f\"Saved to: {out_path}\")\n",
    "else:\n",
    "    print(\"\\n✗ Model does not beat A5 champion.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60dc378a",
   "metadata": {},
   "source": [
    "## 12. Conclusion\n",
    "\n",
    "Summary of tree-based regression experiments for A5b."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cc7f44e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======================================================================\n",
      "A5b TREE-BASED REGRESSION - FINAL SUMMARY\n",
      "======================================================================\n",
      "\n",
      "Models Tested:\n",
      "  1. Decision Tree with depth tuning and ccp_alpha pruning\n",
      "  2. AdaBoost with different loss functions (linear, square, exponential)\n",
      "  3. XGBoost with regularization (reg_alpha, reg_lambda)\n",
      "  4. XGBoost with RandomizedSearchCV optimization\n",
      "  5. LightGBM with num_leaves tuning\n",
      "  6. ExtraTrees (Extremely Randomized Trees)\n",
      "\n",
      "A5 Champion: R² = 0.7204\n",
      "Best A5b Model: LightGBM with R² = 0.7619\n",
      "\n",
      "✓ Improvement: +0.0415\n",
      "\n",
      "Key Findings:\n",
      "  - Feature importance analysis shows consistent top predictors\n",
      "  - Tree-based models provide interpretability through feature importance\n",
      "  - Boosting methods generally outperform single decision trees\n",
      "======================================================================\n"
     ]
    }
   ],
   "source": [
    "print(\"=\" * 70)\n",
    "print(\"A5b TREE-BASED REGRESSION - FINAL SUMMARY\")\n",
    "print(\"=\" * 70)\n",
    "\n",
    "print(\"\\nModels Tested:\")\n",
    "print(\"  1. Decision Tree with depth tuning and ccp_alpha pruning\")\n",
    "print(\"  2. AdaBoost with different loss functions (linear, square, exponential)\")\n",
    "print(\"  3. XGBoost with regularization (reg_alpha, reg_lambda)\")\n",
    "print(\"  4. XGBoost with RandomizedSearchCV optimization\")\n",
    "print(\"  5. LightGBM with num_leaves tuning\")\n",
    "print(\"  6. ExtraTrees (Extremely Randomized Trees)\")\n",
    "\n",
    "print(f\"\\nA5 Champion: R² = {CHAMPION_R2}\")\n",
    "print(f\"Best A5b Model: {best_model_name} with R² = {best_model_test_r2:.4f}\")\n",
    "\n",
    "if best_model_test_r2 > CHAMPION_R2:\n",
    "    print(f\"\\n✓ Improvement: +{best_model_test_r2 - CHAMPION_R2:.4f}\")\n",
    "else:\n",
    "    print(f\"\\n✗ No improvement over champion\")\n",
    "\n",
    "print(\"\\nKey Findings:\")\n",
    "print(\"  - Feature importance analysis shows consistent top predictors\")\n",
    "print(\"  - Tree-based models provide interpretability through feature importance\")\n",
    "print(\"  - Boosting methods generally outperform single decision trees\")\n",
    "print(\"=\" * 70)"
   ]
  }
 ],
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