{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5932b6d2",
   "metadata": {},
   "source": [
    "# Digit Image Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "fbbb7204",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.datasets import load_digits\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier, VotingClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08413240",
   "metadata": {},
   "source": [
    "## Load Data and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "078c4039",
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ffcfbc67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': array([[ 0.,  0.,  5., ...,  0.,  0.,  0.],\n",
       "        [ 0.,  0.,  0., ..., 10.,  0.,  0.],\n",
       "        [ 0.,  0.,  0., ..., 16.,  9.,  0.],\n",
       "        ...,\n",
       "        [ 0.,  0.,  1., ...,  6.,  0.,  0.],\n",
       "        [ 0.,  0.,  2., ..., 12.,  0.,  0.],\n",
       "        [ 0.,  0., 10., ..., 12.,  1.,  0.]], shape=(1797, 64)),\n",
       " 'target': array([0, 1, 2, ..., 8, 9, 8], shape=(1797,)),\n",
       " 'frame': None,\n",
       " 'feature_names': ['pixel_0_0',\n",
       "  'pixel_0_1',\n",
       "  'pixel_0_2',\n",
       "  'pixel_0_3',\n",
       "  'pixel_0_4',\n",
       "  'pixel_0_5',\n",
       "  'pixel_0_6',\n",
       "  'pixel_0_7',\n",
       "  'pixel_1_0',\n",
       "  'pixel_1_1',\n",
       "  'pixel_1_2',\n",
       "  'pixel_1_3',\n",
       "  'pixel_1_4',\n",
       "  'pixel_1_5',\n",
       "  'pixel_1_6',\n",
       "  'pixel_1_7',\n",
       "  'pixel_2_0',\n",
       "  'pixel_2_1',\n",
       "  'pixel_2_2',\n",
       "  'pixel_2_3',\n",
       "  'pixel_2_4',\n",
       "  'pixel_2_5',\n",
       "  'pixel_2_6',\n",
       "  'pixel_2_7',\n",
       "  'pixel_3_0',\n",
       "  'pixel_3_1',\n",
       "  'pixel_3_2',\n",
       "  'pixel_3_3',\n",
       "  'pixel_3_4',\n",
       "  'pixel_3_5',\n",
       "  'pixel_3_6',\n",
       "  'pixel_3_7',\n",
       "  'pixel_4_0',\n",
       "  'pixel_4_1',\n",
       "  'pixel_4_2',\n",
       "  'pixel_4_3',\n",
       "  'pixel_4_4',\n",
       "  'pixel_4_5',\n",
       "  'pixel_4_6',\n",
       "  'pixel_4_7',\n",
       "  'pixel_5_0',\n",
       "  'pixel_5_1',\n",
       "  'pixel_5_2',\n",
       "  'pixel_5_3',\n",
       "  'pixel_5_4',\n",
       "  'pixel_5_5',\n",
       "  'pixel_5_6',\n",
       "  'pixel_5_7',\n",
       "  'pixel_6_0',\n",
       "  'pixel_6_1',\n",
       "  'pixel_6_2',\n",
       "  'pixel_6_3',\n",
       "  'pixel_6_4',\n",
       "  'pixel_6_5',\n",
       "  'pixel_6_6',\n",
       "  'pixel_6_7',\n",
       "  'pixel_7_0',\n",
       "  'pixel_7_1',\n",
       "  'pixel_7_2',\n",
       "  'pixel_7_3',\n",
       "  'pixel_7_4',\n",
       "  'pixel_7_5',\n",
       "  'pixel_7_6',\n",
       "  'pixel_7_7'],\n",
       " 'target_names': array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),\n",
       " 'images': array([[[ 0.,  0.,  5., ...,  1.,  0.,  0.],\n",
       "         [ 0.,  0., 13., ..., 15.,  5.,  0.],\n",
       "         [ 0.,  3., 15., ..., 11.,  8.,  0.],\n",
       "         ...,\n",
       "         [ 0.,  4., 11., ..., 12.,  7.,  0.],\n",
       "         [ 0.,  2., 14., ..., 12.,  0.,  0.],\n",
       "         [ 0.,  0.,  6., ...,  0.,  0.,  0.]],\n",
       " \n",
       "        [[ 0.,  0.,  0., ...,  5.,  0.,  0.],\n",
       "         [ 0.,  0.,  0., ...,  9.,  0.,  0.],\n",
       "         [ 0.,  0.,  3., ...,  6.,  0.,  0.],\n",
       "         ...,\n",
       "         [ 0.,  0.,  1., ...,  6.,  0.,  0.],\n",
       "         [ 0.,  0.,  1., ...,  6.,  0.,  0.],\n",
       "         [ 0.,  0.,  0., ..., 10.,  0.,  0.]],\n",
       " \n",
       "        [[ 0.,  0.,  0., ..., 12.,  0.,  0.],\n",
       "         [ 0.,  0.,  3., ..., 14.,  0.,  0.],\n",
       "         [ 0.,  0.,  8., ..., 16.,  0.,  0.],\n",
       "         ...,\n",
       "         [ 0.,  9., 16., ...,  0.,  0.,  0.],\n",
       "         [ 0.,  3., 13., ..., 11.,  5.,  0.],\n",
       "         [ 0.,  0.,  0., ..., 16.,  9.,  0.]],\n",
       " \n",
       "        ...,\n",
       " \n",
       "        [[ 0.,  0.,  1., ...,  1.,  0.,  0.],\n",
       "         [ 0.,  0., 13., ...,  2.,  1.,  0.],\n",
       "         [ 0.,  0., 16., ..., 16.,  5.,  0.],\n",
       "         ...,\n",
       "         [ 0.,  0., 16., ..., 15.,  0.,  0.],\n",
       "         [ 0.,  0., 15., ..., 16.,  0.,  0.],\n",
       "         [ 0.,  0.,  2., ...,  6.,  0.,  0.]],\n",
       " \n",
       "        [[ 0.,  0.,  2., ...,  0.,  0.,  0.],\n",
       "         [ 0.,  0., 14., ..., 15.,  1.,  0.],\n",
       "         [ 0.,  4., 16., ..., 16.,  7.,  0.],\n",
       "         ...,\n",
       "         [ 0.,  0.,  0., ..., 16.,  2.,  0.],\n",
       "         [ 0.,  0.,  4., ..., 16.,  2.,  0.],\n",
       "         [ 0.,  0.,  5., ..., 12.,  0.,  0.]],\n",
       " \n",
       "        [[ 0.,  0., 10., ...,  1.,  0.,  0.],\n",
       "         [ 0.,  2., 16., ...,  1.,  0.,  0.],\n",
       "         [ 0.,  0., 15., ..., 15.,  0.,  0.],\n",
       "         ...,\n",
       "         [ 0.,  4., 16., ..., 16.,  6.,  0.],\n",
       "         [ 0.,  8., 16., ..., 16.,  8.,  0.],\n",
       "         [ 0.,  1.,  8., ..., 12.,  1.,  0.]]], shape=(1797, 8, 8)),\n",
       " 'DESCR': \".. _digits_dataset:\\n\\nOptical recognition of handwritten digits dataset\\n--------------------------------------------------\\n\\n**Data Set Characteristics:**\\n\\n:Number of Instances: 1797\\n:Number of Attributes: 64\\n:Attribute Information: 8x8 image of integer pixels in the range 0..16.\\n:Missing Attribute Values: None\\n:Creator: E. Alpaydin (alpaydin '@' boun.edu.tr)\\n:Date: July; 1998\\n\\nThis is a copy of the test set of the UCI ML hand-written digits datasets\\nhttps://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits\\n\\nThe data set contains images of hand-written digits: 10 classes where\\neach class refers to a digit.\\n\\nPreprocessing programs made available by NIST were used to extract\\nnormalized bitmaps of handwritten digits from a preprinted form. From a\\ntotal of 43 people, 30 contributed to the training set and different 13\\nto the test set. 32x32 bitmaps are divided into nonoverlapping blocks of\\n4x4 and the number of on pixels are counted in each block. This generates\\nan input matrix of 8x8 where each element is an integer in the range\\n0..16. This reduces dimensionality and gives invariance to small\\ndistortions.\\n\\nFor info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G.\\nT. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.\\nL. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,\\n1994.\\n\\n.. dropdown:: References\\n\\n  - C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their\\n    Applications to Handwritten Digit Recognition, MSc Thesis, Institute of\\n    Graduate Studies in Science and Engineering, Bogazici University.\\n  - E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.\\n  - Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.\\n    Linear dimensionalityreduction using relevance weighted LDA. School of\\n    Electrical and Electronic Engineering Nanyang Technological University.\\n    2005.\\n  - Claudio Gentile. A New Approximate Maximal Margin Classification\\n    Algorithm. NIPS. 2000.\\n\"}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ce157909",
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = digits.data, digits.target"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2932471",
   "metadata": {},
   "source": [
    "### train, test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1617ea4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "223482ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1437, 64)\n",
      "(1437,)\n",
      "(360, 64)\n",
      "(360,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c062c9ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.  0.  3. ... 13.  4.  0.]\n",
      " [ 0.  0.  9. ...  3.  0.  0.]\n",
      " [ 0.  0.  0. ...  6.  0.  0.]\n",
      " ...\n",
      " [ 0.  0.  9. ... 16.  2.  0.]\n",
      " [ 0.  0.  1. ...  0.  0.  0.]\n",
      " [ 0.  0.  1. ...  1.  0.  0.]]\n",
      "\n",
      "\n",
      "[6 0 0 ... 2 7 1]\n",
      "\n",
      "\n",
      "[[ 0.  0.  0. ... 14.  5.  0.]\n",
      " [ 0.  0. 11. ...  1.  0.  0.]\n",
      " [ 0.  0.  8. ...  8.  0.  0.]\n",
      " ...\n",
      " [ 0.  0.  7. ... 10.  0.  0.]\n",
      " [ 0.  0.  7. ... 16.  4.  0.]\n",
      " [ 0.  0. 14. ...  5.  0.  0.]]\n",
      "\n",
      "\n",
      "[6 9 3 7 2 1 5 2 5 2 1 9 4 0 4 2 3 7 8 8 4 3 9 7 5 6 3 5 6 3 4 9 1 4 4 6 9\n",
      " 4 7 6 6 9 1 3 6 1 3 0 6 5 5 1 9 5 6 0 9 0 0 1 0 4 5 2 4 5 7 0 7 5 9 5 5 4\n",
      " 7 0 4 5 5 9 9 0 2 3 8 0 6 4 4 9 1 2 8 3 5 2 9 0 4 4 4 3 5 3 1 3 5 9 4 2 7\n",
      " 7 4 4 1 9 2 7 8 7 2 6 9 4 0 7 2 7 5 8 7 5 7 7 0 6 6 4 2 8 0 9 4 6 9 9 6 9\n",
      " 0 3 5 6 6 0 6 4 3 9 3 9 7 2 9 0 4 5 3 6 5 9 9 8 4 2 1 3 7 7 2 2 3 9 8 0 3\n",
      " 2 2 5 6 9 9 4 1 5 4 2 3 6 4 8 5 9 5 7 8 9 4 8 1 5 4 4 9 6 1 8 6 0 4 5 2 7\n",
      " 4 6 4 5 6 0 3 2 3 6 7 1 5 1 4 7 6 8 8 5 5 1 6 2 8 8 9 9 7 6 2 2 2 3 4 8 8\n",
      " 3 6 0 9 7 7 0 1 0 4 5 1 5 3 6 0 4 1 0 0 3 6 5 9 7 3 5 5 9 9 8 5 3 3 2 0 5\n",
      " 8 3 4 0 2 4 6 4 3 4 5 0 5 2 1 3 1 4 1 1 7 0 1 5 2 1 2 8 7 0 6 4 8 8 5 1 8\n",
      " 4 5 8 7 9 8 5 0 6 2 0 7 9 8 9 5 2 7 7 1 8 7 4 3 8 3 5]\n"
     ]
    }
   ],
   "source": [
    "print(X_train)\n",
    "print(\"\\n\")\n",
    "print(y_train)\n",
    "print(\"\\n\")\n",
    "print(X_test)\n",
    "print(\"\\n\")\n",
    "print(y_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "294813e9",
   "metadata": {},
   "source": [
    "### standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e6206e78",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test) # fit işlemi zaten training veri setine göre yapıldı\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f7d336f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.         -0.34169755 -0.46336049 ...  1.05270303  0.45952251\n",
      "  -0.19710003]\n",
      " [ 0.         -0.34169755  0.78471641 ... -0.64451929 -0.50623083\n",
      "  -0.19710003]\n",
      " [ 0.         -0.34169755 -1.08739895 ... -0.13535259 -0.50623083\n",
      "  -0.19710003]\n",
      " ...\n",
      " [ 0.         -0.34169755  0.78471641 ...  1.56186972 -0.02335416\n",
      "  -0.19710003]\n",
      " [ 0.         -0.34169755 -0.87938613 ... -1.15368598 -0.50623083\n",
      "  -0.19710003]\n",
      " [ 0.         -0.34169755 -0.87938613 ... -0.98396375 -0.50623083\n",
      "  -0.19710003]]\n",
      "\n",
      "\n",
      "[[ 0.         -0.34169755 -1.08739895 ...  1.22242526  0.70096084\n",
      "  -0.19710003]\n",
      " [ 0.         -0.34169755  1.20074205 ... -0.98396375 -0.50623083\n",
      "  -0.19710003]\n",
      " [ 0.         -0.34169755  0.57670359 ...  0.20409187 -0.50623083\n",
      "  -0.19710003]\n",
      " ...\n",
      " [ 0.         -0.34169755  0.36869078 ...  0.54353633 -0.50623083\n",
      "  -0.19710003]\n",
      " [ 0.         -0.34169755  0.36869078 ...  1.56186972  0.45952251\n",
      "  -0.19710003]\n",
      " [ 0.         -0.34169755  1.8247805  ... -0.30507483 -0.50623083\n",
      "  -0.19710003]]\n"
     ]
    }
   ],
   "source": [
    "print(X_train_scaled)\n",
    "print(\"\\n\")\n",
    "print(X_test_scaled)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2aed699",
   "metadata": {},
   "source": [
    "## dimension reduction with PCA  (boyut indirgeme)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "3b63210c",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=0.95) # varyans %95i korunacak şekilde istediğin kadar component oluştur\n",
    "X_train_pca = pca.fit_transform(X_train_scaled)\n",
    "X_test_pca = pca.transform(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c9c46b68",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(360, 64)\n",
      "(360, 40)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[-1.22860261, -3.90755877,  1.10703133, ...,  0.87326704,\n",
       "         0.61671569, -0.01660656],\n",
       "       [ 1.5928407 ,  0.76318394, -2.69392109, ..., -0.48510222,\n",
       "         0.0936227 , -0.16333767],\n",
       "       [ 3.56250204,  0.10242505, -1.30997633, ...,  0.13069552,\n",
       "        -0.20483708,  0.71560138],\n",
       "       ...,\n",
       "       [-0.30603938,  1.65742961, -0.50994937, ...,  0.22478523,\n",
       "         0.07329939,  0.40773833],\n",
       "       [ 1.97859069, -2.46050287, -2.30869361, ...,  0.21371279,\n",
       "        -0.0585146 , -0.39016381],\n",
       "       [ 1.94917266, -0.55075645, -3.5493    , ...,  0.5917301 ,\n",
       "         0.78508384,  0.25457456]], shape=(360, 40))"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(X_test_scaled.shape)\n",
    "print(X_test_pca.shape)\n",
    "X_test_pca"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7ca1fb8",
   "metadata": {},
   "source": [
    "bilgi kaybetmeyecek şekilde 64 özellikten 40 özelliğe düşene kadar indirgeme işlemi yapıldı"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a7a270d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pca_2d = PCA(n_components=2)\n",
    "X_train_pca_2d = pca_2d.fit_transform(X_train_scaled)\n",
    "X_test_pca_2d = pca_2d.fit_transform(X_test_scaled)\n",
    "\n",
    "plt.Figure()\n",
    "for i in np.unique(y_train):\n",
    "    plt.scatter(X_train_pca_2d[y_train == i,0], X_train_pca_2d[y_train == i, 1],\n",
    "                label = digits.target_names[i])\n",
    "\n",
    "plt.xlabel(\"PC 1\")\n",
    "plt.ylabel(\"PC 2\")\n",
    "plt.title(\"PCA ile 2d veri görselleştirme\")\n",
    "plt.legend(title=\"siniflar\", loc = \"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c4218bb",
   "metadata": {},
   "source": [
    "## model training and grid search"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0930230f",
   "metadata": {},
   "source": [
    "### svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "98baecaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA0NDggNTEyIj48IS0tIUZvbnQgQXdlc29tZSBGcmVlIDYuNy4yIGJ5IEBmb250YXdlc29tZSAtIGh0dHBzOi8vZm9udGF3ZXNvbWUuY29tIExpY2Vuc2UgLSBodHRwczovL2ZvbnRhd2Vzb21lLmNvbS9saWNlbnNlL2ZyZWUgQ29weXJpZ2h0IDIwMjUgRm9udGljb25zLCBJbmMuLS0+PHBhdGggZD0iTTIwOCAwTDMzMi4xIDBjMTIuNyAwIDI0LjkgNS4xIDMzLjkgMTQuMWw2Ny45IDY3LjljOSA5IDE0LjEgMjEuMiAxNC4xIDMzLjlMNDQ4IDMzNmMwIDI2LjUtMjEuNSA0OC00OCA0OGwtMTkyIDBjLTI2LjUgMC00OC0yMS41LTQ4LTQ4bDAtMjg4YzAtMjYuNSAyMS41LTQ4IDQ4LTQ4ek00OCAxMjhsODAgMCAwIDY0LTY0IDAgMCAyNTYgMTkyIDAgMC0zMiA2NCAwIDAgNDhjMCAyNi41LTIxLjUgNDgtNDggNDhMNDggNTEyYy0yNi41IDAtNDgtMjEuNS00OC00OEwwIDE3NmMwLTI2LjUgMjEuNS00OCA0OC00OHoiLz48L3N2Zz4=);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=SVC(),\n",
       "             param_grid={&#x27;C&#x27;: [0.1, 1, 10], &#x27;kernel&#x27;: [&#x27;linear&#x27;, &#x27;rbf&#x27;]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GridSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('estimator',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">estimator&nbsp;</td>\n",
       "            <td class=\"value\">SVC()</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('param_grid',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">param_grid&nbsp;</td>\n",
       "            <td class=\"value\">{&#x27;C&#x27;: [0.1, 1, ...], &#x27;kernel&#x27;: [&#x27;linear&#x27;, &#x27;rbf&#x27;]}</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('scoring',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">scoring&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('refit',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">refit&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('cv',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">cv&nbsp;</td>\n",
       "            <td class=\"value\">5</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">verbose&nbsp;</td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('pre_dispatch',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">pre_dispatch&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;2*n_jobs&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('error_score',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">error_score&nbsp;</td>\n",
       "            <td class=\"value\">nan</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('return_train_score',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">return_train_score&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: SVC</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___\"><pre>SVC(C=10)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('C',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">C&nbsp;</td>\n",
       "            <td class=\"value\">10</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('kernel',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">kernel&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;rbf&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('degree',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">degree&nbsp;</td>\n",
       "            <td class=\"value\">3</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('gamma',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">gamma&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;scale&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('coef0',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">coef0&nbsp;</td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('shrinking',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">shrinking&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('probability',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">probability&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">tol&nbsp;</td>\n",
       "            <td class=\"value\">0.001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('cache_size',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">cache_size&nbsp;</td>\n",
       "            <td class=\"value\">200</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('class_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">class_weight&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">verbose&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_iter&nbsp;</td>\n",
       "            <td class=\"value\">-1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('decision_function_shape',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">decision_function_shape&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;ovr&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('break_ties',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">break_ties&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">random_state&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "</script></body>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=SVC(),\n",
       "             param_grid={'C': [0.1, 1, 10], 'kernel': ['linear', 'rbf']})"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_params = {\n",
    "    \"C\":[0.1, 1, 10], \n",
    "    \"kernel\": [\"linear\", \"rbf\"]\n",
    "}\n",
    "\n",
    "svm = SVC()\n",
    "svm_grid = GridSearchCV(svm, svm_params, cv = 5)\n",
    "svm_grid.fit(X_train_pca, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "048a9147",
   "metadata": {},
   "source": [
    "### random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "cba6c700",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-5 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42),\n",
       "             param_grid={&#x27;n_estimators&#x27;: [50, 100, 200]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GridSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('estimator',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">estimator&nbsp;</td>\n",
       "            <td class=\"value\">RandomForestC...ndom_state=42)</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('param_grid',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">param_grid&nbsp;</td>\n",
       "            <td class=\"value\">{&#x27;n_estimators&#x27;: [50, 100, ...]}</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('scoring',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">scoring&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('refit',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">refit&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('cv',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">cv&nbsp;</td>\n",
       "            <td class=\"value\">5</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">verbose&nbsp;</td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('pre_dispatch',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">pre_dispatch&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;2*n_jobs&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('error_score',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">error_score&nbsp;</td>\n",
       "            <td class=\"value\">nan</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('return_train_score',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">return_train_score&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: RandomForestClassifier</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___\"><pre>RandomForestClassifier(n_estimators=200, random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_estimators',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
       "            <td class=\"value\">200</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('criterion',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">criterion&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_depth',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_depth&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_samples_split',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_samples_split&nbsp;</td>\n",
       "            <td class=\"value\">2</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_samples_leaf&nbsp;</td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_weight_fraction_leaf&nbsp;</td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_features',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_features&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;sqrt&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_leaf_nodes&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_impurity_decrease&nbsp;</td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('bootstrap',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">bootstrap&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('oob_score',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">oob_score&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">random_state&nbsp;</td>\n",
       "            <td class=\"value\">42</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">verbose&nbsp;</td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('warm_start',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">warm_start&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('class_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">class_weight&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">ccp_alpha&nbsp;</td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_samples',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_samples&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">monotonic_cst&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "</script></body>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42),\n",
       "             param_grid={'n_estimators': [50, 100, 200]})"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_params = {\n",
    "    \"n_estimators\":[50,100,200]\n",
    "}\n",
    "\n",
    "rf = RandomForestClassifier(random_state=42)\n",
    "rf_grid = GridSearchCV(rf, rf_params, cv = 5)\n",
    "rf_grid.fit(X_train_pca, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e8f23a5",
   "metadata": {},
   "source": [
    "### knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "067b24ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-6 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-6 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-6 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-6 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-6 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-6 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-6 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
       "             param_grid={&#x27;n_neighbors&#x27;: [3, 5, 7]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GridSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('estimator',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">estimator&nbsp;</td>\n",
       "            <td class=\"value\">KNeighborsClassifier()</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('param_grid',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">param_grid&nbsp;</td>\n",
       "            <td class=\"value\">{&#x27;n_neighbors&#x27;: [3, 5, ...]}</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('scoring',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">scoring&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('refit',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">refit&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('cv',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">cv&nbsp;</td>\n",
       "            <td class=\"value\">5</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">verbose&nbsp;</td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('pre_dispatch',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">pre_dispatch&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;2*n_jobs&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('error_score',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">error_score&nbsp;</td>\n",
       "            <td class=\"value\">nan</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('return_train_score',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">return_train_score&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: KNeighborsClassifier</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_neighbors',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_neighbors&nbsp;</td>\n",
       "            <td class=\"value\">3</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('weights',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">weights&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;uniform&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('algorithm',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">algorithm&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('leaf_size',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">leaf_size&nbsp;</td>\n",
       "            <td class=\"value\">30</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('p',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">p&nbsp;</td>\n",
       "            <td class=\"value\">2</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('metric',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">metric&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;minkowski&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('metric_params',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
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       "            <td class=\"param\">metric_params&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
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       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
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       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
       "             param_grid={'n_neighbors': [3, 5, 7]})"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_params = {\n",
    "    \"n_neighbors\":[3,5,7]\n",
    "}\n",
    "\n",
    "knn = KNeighborsClassifier()\n",
    "knn_grid = GridSearchCV(knn, knn_params, cv = 5)\n",
    "knn_grid.fit(X_train_pca, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7726f61",
   "metadata": {},
   "source": [
    "### en iyi parametrelere sahip modelin seçilmesi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "05ab5ca4",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_svm = svm_grid.best_estimator_\n",
    "best_rf = rf_grid.best_estimator_\n",
    "best_knn = knn_grid.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e91a09f",
   "metadata": {},
   "source": [
    "## Voting classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "6e64e5da",
   "metadata": {},
   "outputs": [],
   "source": [
    "voting_clf = VotingClassifier(\n",
    "    estimators=[\n",
    "        (\"svm\", best_svm),\n",
    "        (\"rf\", best_rf),\n",
    "        (\"knn\", best_knn)\n",
    "    ],\n",
    "    voting=\"hard\" # sert ya da yumuşak oylama. \n",
    "    # hard -> çoğunluk oyuna göre\n",
    "    # soft -> ortalama, olasılık toplama, \n",
    "    # modeller olasılık sonucu üretmediği için hard\n",
    ")\n",
    "\n",
    "voting_clf.fit(X_train_pca, y_train)\n",
    "y_pred = voting_clf.predict(X_test_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "58650b03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 9, 3, 7, 2, 1, 5, 2, 5, 2, 1, 9, 4, 0, 4, 2, 3, 7, 8, 8, 4, 3,\n",
       "       9, 7, 5, 6, 3, 5, 6, 3, 4, 9, 1, 4, 4, 6, 9, 4, 7, 6, 6, 9, 1, 3,\n",
       "       6, 1, 3, 0, 6, 5, 5, 1, 9, 5, 6, 0, 9, 0, 0, 1, 0, 4, 5, 2, 4, 5,\n",
       "       7, 0, 7, 5, 9, 9, 5, 4, 7, 0, 4, 5, 5, 9, 9, 0, 2, 3, 8, 0, 6, 4,\n",
       "       4, 9, 1, 2, 8, 3, 5, 2, 9, 0, 4, 4, 4, 3, 5, 3, 1, 3, 5, 9, 4, 2,\n",
       "       7, 7, 4, 4, 1, 9, 2, 7, 8, 7, 2, 6, 9, 4, 0, 7, 2, 7, 5, 8, 7, 5,\n",
       "       7, 9, 0, 6, 6, 4, 2, 8, 0, 9, 4, 6, 9, 9, 6, 9, 0, 3, 5, 6, 6, 0,\n",
       "       6, 4, 2, 9, 3, 7, 7, 2, 9, 0, 4, 5, 3, 6, 5, 9, 9, 8, 4, 2, 1, 3,\n",
       "       7, 7, 2, 2, 3, 9, 8, 0, 3, 2, 2, 5, 6, 9, 9, 4, 1, 5, 4, 2, 3, 6,\n",
       "       4, 8, 5, 9, 5, 7, 8, 9, 4, 8, 1, 5, 4, 4, 9, 6, 1, 8, 6, 0, 4, 5,\n",
       "       2, 7, 4, 6, 4, 5, 6, 0, 3, 2, 3, 6, 7, 1, 5, 1, 4, 7, 6, 8, 8, 5,\n",
       "       5, 1, 6, 2, 8, 8, 9, 5, 7, 6, 2, 2, 2, 3, 4, 8, 8, 3, 6, 0, 9, 7,\n",
       "       7, 0, 1, 0, 4, 5, 1, 5, 3, 6, 0, 4, 1, 0, 0, 3, 6, 5, 9, 7, 3, 5,\n",
       "       5, 9, 9, 8, 5, 3, 3, 2, 0, 5, 8, 3, 4, 0, 2, 4, 6, 4, 3, 4, 5, 0,\n",
       "       5, 2, 1, 3, 1, 4, 1, 1, 7, 0, 1, 5, 2, 1, 2, 8, 7, 0, 6, 4, 8, 8,\n",
       "       5, 1, 2, 4, 5, 8, 7, 9, 8, 6, 0, 6, 2, 0, 7, 9, 8, 9, 5, 2, 7, 7,\n",
       "       1, 8, 7, 4, 3, 8, 3, 5])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dc0e6ba",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "4a58d832",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=digits.target_names)\n",
    "disp.plot(cmap=plt.cm.Blues)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "cbd285ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best svm params: {'C': 10, 'kernel': 'rbf'}\n",
      "Best rf params: {'n_estimators': 200}\n",
      "Best knn params: {'n_neighbors': 3}\n",
      "Voting Classifier accuracy: 0.9805555555555555\n"
     ]
    }
   ],
   "source": [
    "print(f\"Best svm params: {svm_grid.best_params_}\")\n",
    "print(f\"Best rf params: {rf_grid.best_params_}\")\n",
    "print(f\"Best knn params: {knn_grid.best_params_}\")\n",
    "print(f\"Voting Classifier accuracy: {voting_clf.score(X_test_pca, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "00c69e93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['digit_classifier_artifact.joblib']"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "\n",
    "artifact = {\n",
    "    \"scaler\": scaler,\n",
    "    \"pca\": pca,\n",
    "    \"model\": voting_clf,\n",
    "}\n",
    "joblib.dump(artifact, \"digit_classifier_artifact.joblib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e88882d3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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