Support-Vector-Machine.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machine (SVM)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SVMs aim to find the optimal hyperplane that separates data points of different classes while maximizing the margin between the classes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![SVM](./imgs/svm.PNG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.datasets import load_breast_cancer, load_iris\n",
    "from sklearn.model_selection import train_test_split, KFold, cross_val_score, GridSearchCV\n",
    "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With iris dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 4)\n"
     ]
    }
   ],
   "source": [
    "iris_dataset = load_iris()\n",
    "\n",
    "X = iris_dataset.data\n",
    "y = iris_dataset.target\n",
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_svc = SVC()\n",
    "\n",
    "iris_svc.fit(X_train, y_train)\n",
    "y_pred = iris_svc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        19\n",
      "           1       1.00      1.00      1.00        13\n",
      "           2       1.00      1.00      1.00        13\n",
      "\n",
      "    accuracy                           1.00        45\n",
      "   macro avg       1.00      1.00      1.00        45\n",
      "weighted avg       1.00      1.00      1.00        45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(iris_svc.score(X_test, y_test))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.         0.9047619  0.9047619  1.         0.95238095]\n"
     ]
    }
   ],
   "source": [
    "kf = KFold(n_splits=5, shuffle=True, random_state=42)\n",
    "print(cross_val_score(iris_svc, X_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "disp = ConfusionMatrixDisplay(cm)\n",
    "disp.plot()\n",
    "disp.figure_.set_size_inches(w=5, h=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With Diagnostic Wisconsin Breast Cancer dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer_dataset = load_breast_cancer()\n",
    "\n",
    "X = cancer_dataset.data\n",
    "y = cancer_dataset.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer_svc = SVC()\n",
    "\n",
    "cancer_svc.fit(X_train, y_train)\n",
    "y_pred = cancer_svc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.935672514619883\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.83      0.90        63\n",
      "           1       0.91      1.00      0.95       108\n",
      "\n",
      "    accuracy                           0.94       171\n",
      "   macro avg       0.95      0.91      0.93       171\n",
      "weighted avg       0.94      0.94      0.93       171\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(cancer_svc.score(X_test, y_test))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.9        0.9        0.9375     0.86075949 0.87341772]\n"
     ]
    }
   ],
   "source": [
    "kf = KFold(n_splits=5, shuffle=True, random_state=42)\n",
    "print(cross_val_score(cancer_svc, X_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "disp = ConfusionMatrixDisplay(cm)\n",
    "disp.plot()\n",
    "disp.figure_.set_size_inches(w=5, h=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform hyperparameter tunning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    \"C\": np.arange(0, 6, 1),\n",
    "    \"kernel\": ['linear', 'poly', 'rbf', 'sigmoid']\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 24 candidates, totalling 72 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\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-8 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 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-8 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-8 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-8 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-8 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-8 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-8 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-8 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-8 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-8 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 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-8 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 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-8 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 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-8 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-8 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-8 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-8 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-8 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-8 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-8 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-8 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-8 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-8 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-8 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-8 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-8 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 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: 1ex;\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-8 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-8 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-8 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-8 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3, estimator=SVC(),\n",
       "             param_grid={&#x27;C&#x27;: array([0, 1, 2, 3, 4, 5]),\n",
       "                         &#x27;kernel&#x27;: [&#x27;linear&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;, &#x27;sigmoid&#x27;]},\n",
       "             verbose=True)</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-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/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></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=3, estimator=SVC(),\n",
       "             param_grid={&#x27;C&#x27;: array([0, 1, 2, 3, 4, 5]),\n",
       "                         &#x27;kernel&#x27;: [&#x27;linear&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;, &#x27;sigmoid&#x27;]},\n",
       "             verbose=True)</pre></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-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: SVC</label><div class=\"sk-toggleable__content fitted\"><pre>SVC()</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-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SVC()</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=3, estimator=SVC(),\n",
       "             param_grid={'C': array([0, 1, 2, 3, 4, 5]),\n",
       "                         'kernel': ['linear', 'poly', 'rbf', 'sigmoid']},\n",
       "             verbose=True)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search = GridSearchCV(SVC(), params, cv=3, verbose=True)\n",
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 5, 'kernel': 'linear'}\n",
      "0.9590643274853801\n"
     ]
    }
   ],
   "source": [
    "best_estimator = grid_search.best_estimator_\n",
    "print(grid_search.best_params_)\n",
    "print(best_estimator.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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