svc_breast_cancer_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn.preprocessing import scale, StandardScaler\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer = load_breast_cancer()"
   ]
  },
  {
   "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": "code",
   "execution_count": 6,
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer[\"feature_names\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_feat = pd.DataFrame(cancer[\"data\"], columns=cancer[\"feature_names\"])"
   ]
  },
  {
   "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.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_target = pd.DataFrame(cancer[\"target\"], columns=[\"Cancer\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cancer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cancer\n",
       "0       0\n",
       "1       0\n",
       "2       0\n",
       "3       0\n",
       "4       0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_target.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Cancer', ylabel='count'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df_target[\"Cancer\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "frames = [df_feat, df_target]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = pd.concat(frames, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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 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",
       "      <th>Cancer</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>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",
       "      <td>0</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>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",
       "      <td>0</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>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",
       "      <td>0</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>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",
       "      <td>0</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>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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 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 texture  worst perimeter  worst area  \\\n",
       "0                 0.07871  ...          17.33           184.60      2019.0   \n",
       "1                 0.05667  ...          23.41           158.80      1956.0   \n",
       "2                 0.05999  ...          25.53           152.50      1709.0   \n",
       "3                 0.09744  ...          26.50            98.87       567.7   \n",
       "4                 0.05883  ...          16.67           152.20      1575.0   \n",
       "\n",
       "   worst smoothness  worst compactness  worst concavity  worst concave points  \\\n",
       "0            0.1622             0.6656           0.7119                0.2654   \n",
       "1            0.1238             0.1866           0.2416                0.1860   \n",
       "2            0.1444             0.4245           0.4504                0.2430   \n",
       "3            0.2098             0.8663           0.6869                0.2575   \n",
       "4            0.1374             0.2050           0.4000                0.1625   \n",
       "\n",
       "   worst symmetry  worst fractal dimension  Cancer  \n",
       "0          0.4601                  0.11890       0  \n",
       "1          0.2750                  0.08902       0  \n",
       "2          0.3613                  0.08758       0  \n",
       "3          0.6638                  0.17300       0  \n",
       "4          0.2364                  0.07678       0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='mean area', ylabel='mean smoothness'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=\"mean area\", y=\"mean smoothness\", hue=\"Cancer\", data=result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_feat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df_target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's try the SVC model with its default parameters first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnf_matrix = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 56,  10],\n",
       "       [  3, 102]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnf_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 15.0, 'Predicted label')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cnf_matrix, annot=True, cmap=\"YlGnBu\", fmt=\"d\")\n",
    "plt.ylabel(\"Actual label\")\n",
    "plt.xlabel(\"Predicted label\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Let's try it with scaled data now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_X_train = sc.fit_transform(X_train)\n",
    "scaled_X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n"
     ]
    }
   ],
   "source": [
    "svm_model = SVC().fit(scaled_X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = svm_model.predict(scaled_X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnf_matrix = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 64   2]\n",
      " [  1 104]]\n"
     ]
    }
   ],
   "source": [
    "print(cnf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 15.0, 'Predicted label')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cnf_matrix, annot=True, cmap=\"YlGnBu\", fmt=\"d\")\n",
    "plt.ylabel(\"Actual label\")\n",
    "plt.xlabel(\"Predicted label\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.97      0.98        66\n",
      "           1       0.98      0.99      0.99       105\n",
      "\n",
      "    accuracy                           0.98       171\n",
      "   macro avg       0.98      0.98      0.98       171\n",
      "weighted avg       0.98      0.98      0.98       171\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We improved the accuracy of our model with scaled data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's use GridSearchCV to tune our hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {\"C\": [0.1, 1, 10, 100, 1000],\n",
    "             \"gamma\": [10, 5, 3 , 1, 0.1, 0.01, 0.001, 0.0001],\n",
    "             \"kernel\": [\"rbf\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = GridSearchCV(SVC(), param_grid, verbose=3, refit=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 40 candidates, totalling 200 fits\n",
      "[CV] C=0.1, gamma=10, kernel=rbf .....................................\n",
      "[CV] ......... C=0.1, gamma=10, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=0.1, gamma=10, kernel=rbf .....................................\n",
      "[CV] ......... C=0.1, gamma=10, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=0.1, gamma=10, kernel=rbf .....................................\n",
      "[CV] ......... C=0.1, gamma=10, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=0.1, gamma=10, kernel=rbf .....................................\n",
      "[CV] ......... C=0.1, gamma=10, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=0.1, gamma=10, kernel=rbf .....................................\n",
      "[CV] ......... C=0.1, gamma=10, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=0.1, gamma=5, kernel=rbf ......................................\n",
      "[CV] .......... C=0.1, gamma=5, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=0.1, gamma=5, kernel=rbf ......................................\n",
      "[CV] .......... C=0.1, gamma=5, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=0.1, gamma=5, kernel=rbf ......................................\n",
      "[CV] .......... C=0.1, gamma=5, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=0.1, gamma=5, kernel=rbf ......................................\n",
      "[CV] .......... C=0.1, gamma=5, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=0.1, gamma=5, kernel=rbf ......................................\n",
      "[CV] .......... C=0.1, gamma=5, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=0.1, gamma=3, kernel=rbf ......................................\n",
      "[CV] .......... C=0.1, gamma=3, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=0.1, gamma=3, kernel=rbf ......................................\n",
      "[CV] .......... C=0.1, gamma=3, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=0.1, gamma=3, kernel=rbf ......................................\n",
      "[CV] .......... C=0.1, gamma=3, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=0.1, gamma=3, kernel=rbf ......................................\n",
      "[CV] .......... C=0.1, gamma=3, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=0.1, gamma=3, kernel=rbf ......................................\n",
      "[CV] .......... C=0.1, gamma=3, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.912, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.950, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.900, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.962, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.949, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.912, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.963, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.987, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.962, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.688, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.688, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.688, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.684, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.709, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=1, gamma=10, kernel=rbf .......................................\n",
      "[CV] ........... C=1, gamma=10, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=1, gamma=10, kernel=rbf .......................................\n",
      "[CV] ........... C=1, gamma=10, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=1, gamma=10, kernel=rbf .......................................\n",
      "[CV] ........... C=1, gamma=10, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=1, gamma=10, kernel=rbf .......................................\n",
      "[CV] ........... C=1, gamma=10, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=1, gamma=10, kernel=rbf .......................................\n",
      "[CV] ........... C=1, gamma=10, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=1, gamma=5, kernel=rbf ........................................\n",
      "[CV] ............ C=1, gamma=5, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=1, gamma=5, kernel=rbf ........................................\n",
      "[CV] ............ C=1, gamma=5, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=1, gamma=5, kernel=rbf ........................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ............ C=1, gamma=5, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=1, gamma=5, kernel=rbf ........................................\n",
      "[CV] ............ C=1, gamma=5, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=1, gamma=5, kernel=rbf ........................................\n",
      "[CV] ............ C=1, gamma=5, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=1, gamma=3, kernel=rbf ........................................\n",
      "[CV] ............ C=1, gamma=3, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=1, gamma=3, kernel=rbf ........................................\n",
      "[CV] ............ C=1, gamma=3, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=1, gamma=3, kernel=rbf ........................................\n",
      "[CV] ............ C=1, gamma=3, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=1, gamma=3, kernel=rbf ........................................\n",
      "[CV] ............ C=1, gamma=3, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=1, gamma=3, kernel=rbf ........................................\n",
      "[CV] ............ C=1, gamma=3, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.950, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.950, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.963, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.987, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.950, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.988, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.975, 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=0.963, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.987, 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=   0.0s\n",
      "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
      "[CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.688, total=   0.0s\n",
      "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.688, total=   0.0s\n",
      "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
      "[CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.700, total=   0.0s\n",
      "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
      "[CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.696, total=   0.0s\n",
      "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
      "[CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.709, total=   0.0s\n",
      "[CV] C=10, gamma=10, kernel=rbf ......................................\n",
      "[CV] .......... C=10, gamma=10, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=10, gamma=10, kernel=rbf ......................................\n",
      "[CV] .......... C=10, gamma=10, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=10, gamma=10, kernel=rbf ......................................\n",
      "[CV] .......... C=10, gamma=10, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=10, gamma=10, kernel=rbf ......................................\n",
      "[CV] .......... C=10, gamma=10, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=10, gamma=10, kernel=rbf ......................................\n",
      "[CV] .......... C=10, gamma=10, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=10, gamma=5, kernel=rbf .......................................\n",
      "[CV] ........... C=10, gamma=5, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=10, gamma=5, kernel=rbf .......................................\n",
      "[CV] ........... C=10, gamma=5, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=10, gamma=5, kernel=rbf .......................................\n",
      "[CV] ........... C=10, gamma=5, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=10, gamma=5, kernel=rbf .......................................\n",
      "[CV] ........... C=10, gamma=5, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=10, gamma=5, kernel=rbf .......................................\n",
      "[CV] ........... C=10, gamma=5, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=10, gamma=3, kernel=rbf .......................................\n",
      "[CV] ........... C=10, gamma=3, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=10, gamma=3, kernel=rbf .......................................\n",
      "[CV] ........... C=10, gamma=3, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=10, gamma=3, kernel=rbf .......................................\n",
      "[CV] ........... C=10, gamma=3, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=10, gamma=3, kernel=rbf .......................................\n",
      "[CV] ........... C=10, gamma=3, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=10, gamma=3, kernel=rbf .......................................\n",
      "[CV] ........... C=10, gamma=3, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.950, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.963, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.987, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.987, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.963, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.963, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.987, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.950, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.988, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.987, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] ....... C=10, gamma=0.001, 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=0.912, total=   0.0s\n",
      "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
      "[CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.963, 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=0.987, total=   0.0s\n",
      "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
      "[CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.962, total=   0.0s\n",
      "[CV] C=100, gamma=10, kernel=rbf .....................................\n",
      "[CV] ......... C=100, gamma=10, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=100, gamma=10, kernel=rbf .....................................\n",
      "[CV] ......... C=100, gamma=10, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=100, gamma=10, kernel=rbf .....................................\n",
      "[CV] ......... C=100, gamma=10, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=100, gamma=10, kernel=rbf .....................................\n",
      "[CV] ......... C=100, gamma=10, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=100, gamma=10, kernel=rbf .....................................\n",
      "[CV] ......... C=100, gamma=10, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=100, gamma=5, kernel=rbf ......................................\n",
      "[CV] .......... C=100, gamma=5, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=100, gamma=5, kernel=rbf ......................................\n",
      "[CV] .......... C=100, gamma=5, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=100, gamma=5, kernel=rbf ......................................\n",
      "[CV] .......... C=100, gamma=5, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=100, gamma=5, kernel=rbf ......................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .......... C=100, gamma=5, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=100, gamma=5, kernel=rbf ......................................\n",
      "[CV] .......... C=100, gamma=5, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=100, gamma=3, kernel=rbf ......................................\n",
      "[CV] .......... C=100, gamma=3, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=100, gamma=3, kernel=rbf ......................................\n",
      "[CV] .......... C=100, gamma=3, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=100, gamma=3, kernel=rbf ......................................\n",
      "[CV] .......... C=100, gamma=3, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=100, gamma=3, kernel=rbf ......................................\n",
      "[CV] .......... C=100, gamma=3, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=100, gamma=3, kernel=rbf ......................................\n",
      "[CV] .......... C=100, gamma=3, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.950, total=   0.0s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.963, total=   0.0s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.987, total=   0.0s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.938, total=   0.0s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.949, total=   0.0s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.950, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.987, total=   0.0s\n",
      "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.950, total=   0.0s\n",
      "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.988, total=   0.0s\n",
      "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=100, gamma=0.0001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.987, 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=1000, gamma=10, kernel=rbf ....................................\n",
      "[CV] ........ C=1000, gamma=10, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=1000, gamma=10, kernel=rbf ....................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ........ C=1000, gamma=10, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=1000, gamma=10, kernel=rbf ....................................\n",
      "[CV] ........ C=1000, gamma=10, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=1000, gamma=10, kernel=rbf ....................................\n",
      "[CV] ........ C=1000, gamma=10, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=1000, gamma=10, kernel=rbf ....................................\n",
      "[CV] ........ C=1000, gamma=10, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=1000, gamma=5, kernel=rbf .....................................\n",
      "[CV] ......... C=1000, gamma=5, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=1000, gamma=5, kernel=rbf .....................................\n",
      "[CV] ......... C=1000, gamma=5, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=1000, gamma=5, kernel=rbf .....................................\n",
      "[CV] ......... C=1000, gamma=5, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=1000, gamma=5, kernel=rbf .....................................\n",
      "[CV] ......... C=1000, gamma=5, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=1000, gamma=5, kernel=rbf .....................................\n",
      "[CV] ......... C=1000, gamma=5, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=1000, gamma=3, kernel=rbf .....................................\n",
      "[CV] ......... C=1000, gamma=3, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=1000, gamma=3, kernel=rbf .....................................\n",
      "[CV] ......... C=1000, gamma=3, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=1000, gamma=3, kernel=rbf .....................................\n",
      "[CV] ......... C=1000, gamma=3, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=1000, gamma=3, kernel=rbf .....................................\n",
      "[CV] ......... C=1000, gamma=3, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=1000, gamma=3, kernel=rbf .....................................\n",
      "[CV] ......... C=1000, gamma=3, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
      "[CV] ......... C=1000, gamma=1, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
      "[CV] ......... C=1000, gamma=1, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
      "[CV] ......... C=1000, gamma=1, kernel=rbf, score=0.637, total=   0.0s\n",
      "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
      "[CV] ......... C=1000, gamma=1, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=1000, gamma=1, kernel=rbf .....................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ......... C=1000, gamma=1, kernel=rbf, score=0.633, total=   0.0s\n",
      "[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
      "[CV] ....... C=1000, gamma=0.1, kernel=rbf, score=0.950, total=   0.0s\n",
      "[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
      "[CV] ....... C=1000, gamma=0.1, kernel=rbf, score=0.963, total=   0.0s\n",
      "[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
      "[CV] ....... C=1000, gamma=0.1, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
      "[CV] ....... C=1000, gamma=0.1, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
      "[CV] ....... C=1000, gamma=0.1, kernel=rbf, score=0.987, total=   0.0s\n",
      "[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
      "[CV] ...... C=1000, gamma=0.01, kernel=rbf, score=0.950, total=   0.0s\n",
      "[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
      "[CV] ...... C=1000, gamma=0.01, kernel=rbf, score=0.950, total=   0.0s\n",
      "[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
      "[CV] ...... C=1000, gamma=0.01, kernel=rbf, score=0.938, total=   0.0s\n",
      "[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
      "[CV] ...... C=1000, gamma=0.01, kernel=rbf, score=0.937, total=   0.0s\n",
      "[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
      "[CV] ...... C=1000, gamma=0.01, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
      "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
      "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.963, total=   0.0s\n",
      "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
      "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
      "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.949, total=   0.0s\n",
      "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
      "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
      "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.950, total=   0.0s\n",
      "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
      "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
      "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
      "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
      "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.987, total=   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "[Parallel(n_jobs=1)]: Done 200 out of 200 | elapsed:    1.5s finished\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=SVC(),\n",
       "             param_grid={'C': [0.1, 1, 10, 100, 1000],\n",
       "                         'gamma': [10, 5, 3, 1, 0.1, 0.01, 0.001, 0.0001],\n",
       "                         'kernel': ['rbf']},\n",
       "             verbose=3)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.fit(scaled_X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 10, 'gamma': 0.001, 'kernel': 'rbf'}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=10, gamma=0.001)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_pred = grid.predict(scaled_X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnf_matrix = confusion_matrix(y_test, grid_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 60,   6],\n",
       "       [  0, 105]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnf_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.91      0.95        66\n",
      "           1       0.95      1.00      0.97       105\n",
      "\n",
      "    accuracy                           0.96       171\n",
      "   macro avg       0.97      0.95      0.96       171\n",
      "weighted avg       0.97      0.96      0.96       171\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, grid_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Overall accuracy seems worse but this is a data about breast cancer and our tuned method\n",
    "# performed better in False Negative errors which is probably extremely valuable when dealing\n",
    "# with health related data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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