lecture06-Titanic-DummiesPandasNaiveBayes.ipynb

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    "# Processing"
   ]
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Name</th>\n",
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       "      <th>Sex</th>\n",
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       "      <th>SibSp</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
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       "      <th>4</th>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>3</td>\n",
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       "      <td>35.0</td>\n",
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       "      <td>373450</td>\n",
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       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
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       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
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       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
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       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>0</td>\n",
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       "<p>891 rows × 12 columns</p>\n",
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       "     PassengerId                                               Name  Pclass  \\\n",
       "0              1                            Braund, Mr. Owen Harris       3   \n",
       "1              2  Cumings, Mrs. John Bradley (Florence Briggs Th...       1   \n",
       "2              3                             Heikkinen, Miss. Laina       3   \n",
       "3              4       Futrelle, Mrs. Jacques Heath (Lily May Peel)       1   \n",
       "4              5                           Allen, Mr. William Henry       3   \n",
       "..           ...                                                ...     ...   \n",
       "886          887                              Montvila, Rev. Juozas       2   \n",
       "887          888                       Graham, Miss. Margaret Edith       1   \n",
       "888          889           Johnston, Miss. Catherine Helen \"Carrie\"       3   \n",
       "889          890                              Behr, Mr. Karl Howell       1   \n",
       "890          891                                Dooley, Mr. Patrick       3   \n",
       "\n",
       "        Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked  \\\n",
       "0      male  22.0      1      0         A/5 21171   7.2500   NaN        S   \n",
       "1    female  38.0      1      0          PC 17599  71.2833   C85        C   \n",
       "2    female  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S   \n",
       "3    female  35.0      1      0            113803  53.1000  C123        S   \n",
       "4      male  35.0      0      0            373450   8.0500   NaN        S   \n",
       "..      ...   ...    ...    ...               ...      ...   ...      ...   \n",
       "886    male  27.0      0      0            211536  13.0000   NaN        S   \n",
       "887  female  19.0      0      0            112053  30.0000   B42        S   \n",
       "888  female   NaN      1      2        W./C. 6607  23.4500   NaN        S   \n",
       "889    male  26.0      0      0            111369  30.0000  C148        C   \n",
       "890    male  32.0      0      0            370376   7.7500   NaN        Q   \n",
       "\n",
       "     Survived  \n",
       "0           0  \n",
       "1           1  \n",
       "2           1  \n",
       "3           1  \n",
       "4           0  \n",
       "..        ...  \n",
       "886         0  \n",
       "887         1  \n",
       "888         0  \n",
       "889         1  \n",
       "890         0  \n",
       "\n",
       "[891 rows x 12 columns]"
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "df = pd.read_csv (r'C:\\Users\\a_ade\\Documents\\Fall2021\\CSC276\\titanic.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
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       "      <td>7.7500</td>\n",
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       "<p>891 rows × 5 columns</p>\n",
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      "text/plain": [
       "     Pclass     Sex   Age     Fare  Survived\n",
       "0         3    male  22.0   7.2500         0\n",
       "1         1  female  38.0  71.2833         1\n",
       "2         3  female  26.0   7.9250         1\n",
       "3         1  female  35.0  53.1000         1\n",
       "4         3    male  35.0   8.0500         0\n",
       "..      ...     ...   ...      ...       ...\n",
       "886       2    male  27.0  13.0000         0\n",
       "887       1  female  19.0  30.0000         1\n",
       "888       3  female   NaN  23.4500         0\n",
       "889       1    male  26.0  30.0000         1\n",
       "890       3    male  32.0   7.7500         0\n",
       "\n",
       "[891 rows x 5 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "df2= df.drop(['PassengerId', 'Name','SibSp', 'Parch', 'Ticket', 'Embarked', 'Cabin'],axis='columns')\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
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    {
     "data": {
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       "0      0\n",
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       "2      1\n",
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       "      ..\n",
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       "Name: Survived, Length: 891, dtype: int64"
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   ],
   "source": [
    "target=df.Survived\n",
    "target"
   ]
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      ],
      "text/plain": [
       "     Pclass     Sex   Age     Fare\n",
       "0         3    male  22.0   7.2500\n",
       "1         1  female  38.0  71.2833\n",
       "2         3  female  26.0   7.9250\n",
       "3         1  female  35.0  53.1000\n",
       "4         3    male  35.0   8.0500\n",
       "..      ...     ...   ...      ...\n",
       "886       2    male  27.0  13.0000\n",
       "887       1  female  19.0  30.0000\n",
       "888       3  female   NaN  23.4500\n",
       "889       1    male  26.0  30.0000\n",
       "890       3    male  32.0   7.7500\n",
       "\n",
       "[891 rows x 4 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input=df2.drop(['Survived'], axis='columns')\n",
    "input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x0000028D1AA15B80>"
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    }
   ],
   "source": [
    "df.groupby(['Survived'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas Dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     female  male\n",
       "0         0     1\n",
       "1         1     0\n",
       "2         1     0\n",
       "3         1     0\n",
       "4         0     1\n",
       "..      ...   ...\n",
       "886       0     1\n",
       "887       1     0\n",
       "888       1     0\n",
       "889       0     1\n",
       "890       0     1\n",
       "\n",
       "[891 rows x 2 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummies= pd.get_dummies(input.Sex)\n",
    "dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>female</th>\n",
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       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>886</th>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass     Sex   Age     Fare  female  male\n",
       "0         3    male  22.0   7.2500       0     1\n",
       "1         1  female  38.0  71.2833       1     0\n",
       "2         3  female  26.0   7.9250       1     0\n",
       "3         1  female  35.0  53.1000       1     0\n",
       "4         3    male  35.0   8.0500       0     1\n",
       "..      ...     ...   ...      ...     ...   ...\n",
       "886       2    male  27.0  13.0000       0     1\n",
       "887       1  female  19.0  30.0000       1     0\n",
       "888       3  female   NaN  23.4500       1     0\n",
       "889       1    male  26.0  30.0000       0     1\n",
       "890       3    male  32.0   7.7500       0     1\n",
       "\n",
       "[891 rows x 6 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#connect two dataframe together\n",
    "\n",
    "inputs= pd.concat([input,dummies],axis='columns')\n",
    "inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age'], dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#any column that has NaN value\n",
    "\n",
    "inputs.columns[inputs.isna().any()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
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   "outputs": [
    {
     "data": {
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       "      <td>13.0000</td>\n",
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       "      <td>23.4500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass     Sex        Age     Fare  female  male\n",
       "0         3    male  22.000000   7.2500       0     1\n",
       "1         1  female  38.000000  71.2833       1     0\n",
       "2         3  female  26.000000   7.9250       1     0\n",
       "3         1  female  35.000000  53.1000       1     0\n",
       "4         3    male  35.000000   8.0500       0     1\n",
       "..      ...     ...        ...      ...     ...   ...\n",
       "886       2    male  27.000000  13.0000       0     1\n",
       "887       1  female  19.000000  30.0000       1     0\n",
       "888       3  female  29.699118  23.4500       1     0\n",
       "889       1    male  26.000000  30.0000       0     1\n",
       "890       3    male  32.000000   7.7500       0     1\n",
       "\n",
       "[891 rows x 6 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs.Age= inputs.Age.fillna(inputs.Age.mean())\n",
    "inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>887</th>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>3</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>3</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass        Age     Fare  female\n",
       "0         3  22.000000   7.2500       0\n",
       "1         1  38.000000  71.2833       1\n",
       "2         3  26.000000   7.9250       1\n",
       "3         1  35.000000  53.1000       1\n",
       "4         3  35.000000   8.0500       0\n",
       "..      ...        ...      ...     ...\n",
       "886       2  27.000000  13.0000       0\n",
       "887       1  19.000000  30.0000       1\n",
       "888       3  29.699118  23.4500       1\n",
       "889       1  26.000000  30.0000       0\n",
       "890       3  32.000000   7.7500       0\n",
       "\n",
       "[891 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs=inputs.drop(['Sex','male'],axis='columns')\n",
    "inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass   Age     Fare  female\n",
       "0       3  22.0   7.2500       0\n",
       "1       1  38.0  71.2833       1\n",
       "2       3  26.0   7.9250       1\n",
       "3       1  35.0  53.1000       1\n",
       "4       3  35.0   8.0500       0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs.Age= inputs.Age.fillna(inputs.Age.mean())\n",
    "inputs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SkLearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#target column is Survived\n",
    "\n",
    "x_train,x_test,y_train , y_test = train_test_split(inputs, target,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "model = GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "891"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "179"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#for the 1st dataframe is called X_train , x_test\n",
    "\n",
    "len(x_train)\n",
    "\n",
    "len(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "748    0\n",
       "116    0\n",
       "446    1\n",
       "611    0\n",
       "474    0\n",
       "608    1\n",
       "773    0\n",
       "111    0\n",
       "196    0\n",
       "244    0\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# model.score(x_train, x_test)\n",
    "\n",
    "y_test[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#for the 2nd dataframe is called y_train , y_test\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 0, 1, 0, 1, 0, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 0 is not survived , 1 is survived\n",
    "\n",
    "model.predict(x_test[0:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.26078486e-04, 9.99773922e-01],\n",
       "       [9.66544709e-01, 3.34552907e-02],\n",
       "       [2.36684288e-01, 7.63315712e-01],\n",
       "       [9.72315485e-01, 2.76845146e-02],\n",
       "       [4.04095827e-01, 5.95904173e-01],\n",
       "       [9.30312277e-01, 6.96877234e-02],\n",
       "       [1.36613042e-02, 9.86338696e-01],\n",
       "       [9.22721977e-01, 7.72780234e-02],\n",
       "       [9.65809645e-01, 3.41903552e-02],\n",
       "       [3.76769073e-01, 6.23230927e-01]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_proba(x_test[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate the score using cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.78321678, 0.81118881, 0.76056338, 0.73239437, 0.81690141])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "cross_val_score(GaussianNB(), x_train, y_train, cv=5 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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