18 SVM - Breast Cancer Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machine - Breast Cancer Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/breast_cancer.jpg\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import warnings\n",
    "#warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer = load_breast_cancer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data set is presented in a dictionary form:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename'])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can grab information and arrays out of this dictionary to set up our data frame and understanding of the features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(cancer['DESCR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
       "       'mean smoothness', 'mean compactness', 'mean concavity',\n",
       "       'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
       "       'radius error', 'texture error', 'perimeter error', 'area error',\n",
       "       'smoothness error', 'compactness error', 'concavity error',\n",
       "       'concave points error', 'symmetry error',\n",
       "       'fractal dimension error', 'worst radius', 'worst texture',\n",
       "       'worst perimeter', 'worst area', 'worst smoothness',\n",
       "       'worst compactness', 'worst concavity', 'worst concave points',\n",
       "       'worst symmetry', 'worst fractal dimension'], dtype='<U23')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer['feature_names']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 569 entries, 0 to 568\n",
      "Data columns (total 30 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   mean radius              569 non-null    float64\n",
      " 1   mean texture             569 non-null    float64\n",
      " 2   mean perimeter           569 non-null    float64\n",
      " 3   mean area                569 non-null    float64\n",
      " 4   mean smoothness          569 non-null    float64\n",
      " 5   mean compactness         569 non-null    float64\n",
      " 6   mean concavity           569 non-null    float64\n",
      " 7   mean concave points      569 non-null    float64\n",
      " 8   mean symmetry            569 non-null    float64\n",
      " 9   mean fractal dimension   569 non-null    float64\n",
      " 10  radius error             569 non-null    float64\n",
      " 11  texture error            569 non-null    float64\n",
      " 12  perimeter error          569 non-null    float64\n",
      " 13  area error               569 non-null    float64\n",
      " 14  smoothness error         569 non-null    float64\n",
      " 15  compactness error        569 non-null    float64\n",
      " 16  concavity error          569 non-null    float64\n",
      " 17  concave points error     569 non-null    float64\n",
      " 18  symmetry error           569 non-null    float64\n",
      " 19  fractal dimension error  569 non-null    float64\n",
      " 20  worst radius             569 non-null    float64\n",
      " 21  worst texture            569 non-null    float64\n",
      " 22  worst perimeter          569 non-null    float64\n",
      " 23  worst area               569 non-null    float64\n",
      " 24  worst smoothness         569 non-null    float64\n",
      " 25  worst compactness        569 non-null    float64\n",
      " 26  worst concavity          569 non-null    float64\n",
      " 27  worst concave points     569 non-null    float64\n",
      " 28  worst symmetry           569 non-null    float64\n",
      " 29  worst fractal dimension  569 non-null    float64\n",
      "dtypes: float64(30)\n",
      "memory usage: 133.5 KB\n"
     ]
    }
   ],
   "source": [
    "df_feat = pd.DataFrame(cancer['data'],columns=cancer['feature_names'])\n",
    "\n",
    "df_feat.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   mean fractal dimension  ...  worst radius  worst texture  worst perimeter  \\\n",
       "0                 0.07871  ...         25.38          17.33           184.60   \n",
       "1                 0.05667  ...         24.99          23.41           158.80   \n",
       "2                 0.05999  ...         23.57          25.53           152.50   \n",
       "3                 0.09744  ...         14.91          26.50            98.87   \n",
       "4                 0.05883  ...         22.54          16.67           152.20   \n",
       "\n",
       "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
       "0      2019.0            0.1622             0.6656           0.7119   \n",
       "1      1956.0            0.1238             0.1866           0.2416   \n",
       "2      1709.0            0.1444             0.4245           0.4504   \n",
       "3       567.7            0.2098             0.8663           0.6869   \n",
       "4      1575.0            0.1374             0.2050           0.4000   \n",
       "\n",
       "   worst concave points  worst symmetry  worst fractal dimension  \n",
       "0                0.2654          0.4601                  0.11890  \n",
       "1                0.1860          0.2750                  0.08902  \n",
       "2                0.2430          0.3613                  0.08758  \n",
       "3                0.2575          0.6638                  0.17300  \n",
       "4                0.1625          0.2364                  0.07678  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_feat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
       "       'mean smoothness', 'mean compactness', 'mean concavity',\n",
       "       'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
       "       'radius error', 'texture error', 'perimeter error', 'area error',\n",
       "       'smoothness error', 'compactness error', 'concavity error',\n",
       "       'concave points error', 'symmetry error',\n",
       "       'fractal dimension error', 'worst radius', 'worst texture',\n",
       "       'worst perimeter', 'worst area', 'worst smoothness',\n",
       "       'worst compactness', 'worst concavity', 'worst concave points',\n",
       "       'worst symmetry', 'worst fractal dimension'], dtype='<U23')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer['feature_names']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x217a95bc040>"
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     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 900x900 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df_feat, vars=['mean radius','mean texture','mean perimeter','mean area','mean smoothness'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_feat\n",
    "\n",
    "y = cancer['target']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train the Support Vector Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a Model\n",
    "model = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the Model\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictions and Evaluations\n",
    "\n",
    "Now let's predict using the trained model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make Predictions\n",
    "predictions = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 56  10]\n",
      " [  3 102]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.85      0.90        66\n",
      "           1       0.91      0.97      0.94       105\n",
      "\n",
      "    accuracy                           0.92       171\n",
      "   macro avg       0.93      0.91      0.92       171\n",
      "weighted avg       0.93      0.92      0.92       171\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Woah! Notice that we are classifying everything into a single class! This means our model needs to have it parameters adjusted (it may also help to normalize the data).\n",
    "\n",
    "We can search for parameters using a GridSearch!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gridsearch\n",
    "\n",
    "Finding the right parameters (like what C or gamma values to use) is a tricky task! But luckily, we can be a little lazy and just try a bunch of combinations and see what works best! This idea of creating a 'grid' of parameters and just trying out all the possible combinations is called a Gridsearch, this method is common enough that Scikit-learn has this functionality built in with GridSearchCV! The CV stands for cross-validation which is the GridSearchCV takes a dictionary that describes the parameters that should be tried and a model to train. The grid of parameters is defined as a dictionary, where the keys are the parameters and the values are the settings to be tested. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {'C': [0.1,1, 10, 100, 1000], 'gamma': [1,0.1,0.01,0.001,0.0001], 'kernel': ['rbf']} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the great things about GridSearchCV is that it is a **meta-estimator**. It takes an estimator like SVC, and creates a new estimator, that behaves exactly the same - in this case, like a classifier. You should add refit=True and choose verbose to whatever number you want, higher the number, the more verbose (verbose just means the text output describing the process)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = GridSearchCV(SVC(),param_grid,refit=True,verbose=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What fit does is a bit more involved then usual. First, it runs the same loop with cross-validation, to find the best parameter combination. Once it has the best combination, it runs fit again on all data passed to fit (without cross-validation), to built a single new model using the best parameter setting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 25 candidates, totalling 125 fits\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.637, total=   0.1s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.637, total=   0.1s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.625, total=   0.2s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.633, total=   0.1s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.633, total=   0.1s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.637, total=   0.1s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.637, total=   0.1s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.625, total=   0.1s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.633, total=   0.1s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.633, total=   0.1s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.637, total=   0.1s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.637, total=   0.1s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.625, total=   0.1s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.633, total=   0.1s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.633, total=   0.1s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.637, total=   0.1s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.633, total=   0.1s\n",
      "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.887, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.938, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.963, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.962, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.886, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.637, total=   0.1s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.637, total=   0.1s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.625, total=   0.1s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.633, total=   0.1s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.633, total=   0.1s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.637, total=   0.1s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.637, total=   0.1s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.625, total=   0.1s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.633, total=   0.1s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.633, total=   0.1s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.637, total=   0.1s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.637, total=   0.1s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.625, total=   0.1s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.633, total=   0.1s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.633, total=   0.1s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.900, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.912, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.925, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.962, total=   0.1s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.937, total=   0.0s\n",
      "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
      "[CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.912, total=   0.0s\n",
      "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
      "[CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.950, total=   0.0s\n",
      "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
      "[CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
      "[CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.962, total=   0.0s\n",
      "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
      "[CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.937, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.637, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.637, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.625, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.633, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.633, total=   0.1s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.637, total=   0.1s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.637, total=   0.1s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.625, total=   0.1s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.633, total=   0.1s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.633, total=   0.1s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.637, total=   0.1s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.637, total=   0.1s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.613, total=   0.1s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.633, total=   0.1s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.633, total=   0.1s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.887, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.912, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.900, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.937, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.924, total=   0.0s\n",
      "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
      "[CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.950, total=   0.0s\n",
      "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
      "[CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.912, total=   0.0s\n",
      "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
      "[CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
      "[CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.949, total=   0.0s\n",
      "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
      "[CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.949, total=   0.0s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.637, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.637, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.625, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.633, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.633, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.637, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.637, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.625, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.633, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.633, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.637, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.637, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.613, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.633, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.633, total=   0.1s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.887, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.912, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.900, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.937, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.924, total=   0.0s\n",
      "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.925, total=   0.0s\n",
      "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.912, total=   0.0s\n",
      "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.937, total=   0.0s\n",
      "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.949, total=   0.0s\n",
      "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
      "[CV] ......... C=1000, gamma=1, kernel=rbf, score=0.637, total=   0.1s\n",
      "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
      "[CV] ......... C=1000, gamma=1, kernel=rbf, score=0.637, total=   0.1s\n",
      "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
      "[CV] ......... C=1000, gamma=1, kernel=rbf, score=0.625, total=   0.1s\n",
      "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
      "[CV] ......... C=1000, gamma=1, kernel=rbf, score=0.633, total=   0.1s\n",
      "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
      "[CV] ......... C=1000, gamma=1, kernel=rbf, score=0.633, total=   0.1s\n",
      "[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
      "[CV] ....... C=1000, gamma=0.1, kernel=rbf, score=0.637, total=   0.1s\n",
      "[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
      "[CV] ....... C=1000, gamma=0.1, kernel=rbf, score=0.637, total=   0.1s\n",
      "[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
      "[CV] ....... C=1000, gamma=0.1, kernel=rbf, score=0.625, total=   0.1s\n",
      "[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
      "[CV] ....... C=1000, gamma=0.1, kernel=rbf, score=0.633, total=   0.1s\n",
      "[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
      "[CV] ....... C=1000, gamma=0.1, kernel=rbf, score=0.633, total=   0.1s\n",
      "[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
      "[CV] ...... C=1000, gamma=0.01, kernel=rbf, score=0.637, total=   0.1s\n",
      "[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
      "[CV] ...... C=1000, gamma=0.01, kernel=rbf, score=0.637, total=   0.1s\n",
      "[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
      "[CV] ...... C=1000, gamma=0.01, kernel=rbf, score=0.613, total=   0.1s\n",
      "[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
      "[CV] ...... C=1000, gamma=0.01, kernel=rbf, score=0.633, total=   0.1s\n",
      "[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
      "[CV] ...... C=1000, gamma=0.01, kernel=rbf, score=0.633, total=   0.1s\n",
      "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
      "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.887, total=   0.0s\n",
      "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
      "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.912, total=   0.0s\n",
      "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
      "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.900, total=   0.1s\n",
      "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
      "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.937, total=   0.0s\n",
      "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
      "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.924, total=   0.0s\n",
      "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
      "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.938, total=   0.0s\n",
      "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
      "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.912, total=   0.0s\n",
      "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.963, total=   0.0s\n",
      "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
      "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.924, total=   0.0s\n",
      "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
      "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.962, total=   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 125 out of 125 | elapsed:    6.7s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=SVC(),\n",
       "             param_grid={'C': [0.1, 1, 10, 100, 1000],\n",
       "                         'gamma': [1, 0.1, 0.01, 0.001, 0.0001],\n",
       "                         'kernel': ['rbf']},\n",
       "             verbose=3)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the Grid\n",
    "grid.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can inspect the best parameters found by GridSearchCV in the best_params_ attribute, and the best estimator in the best_estimator_ attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1, 'gamma': 0.0001, 'kernel': 'rbf'}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1, gamma=0.0001)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then you can re-run predictions on this grid object just like you would with a normal model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_predictions = grid.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 59   7]\n",
      " [  4 101]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,grid_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.89      0.91        66\n",
      "           1       0.94      0.96      0.95       105\n",
      "\n",
      "    accuracy                           0.94       171\n",
      "   macro avg       0.94      0.93      0.93       171\n",
      "weighted avg       0.94      0.94      0.94       171\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,grid_predictions))"
   ]
  }
 ],
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