model_evaluation_with_bias_and_variance.ipynb

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     "text": [
      "C:\\Users\\wodnj\\AppData\\Local\\Temp\\ipykernel_7184\\3335876068.py:2: DeprecationWarning: \n",
      "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n",
      "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n",
      "but was not found to be installed on your system.\n",
      "If this would cause problems for you,\n",
      "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n",
      "        \n",
      "  import pandas as pd\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "np.set_printoptions(suppress = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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    "id": "b3pEMxxCkHoS"
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(action = 'ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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    {
     "data": {
      "text/plain": [
       "'1.4.1.post1'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sklearn\n",
    "sklearn.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZzzSjsa18CX8"
   },
   "source": [
    "Heart Disease data from uci ml website  \n",
    "https://archive.ics.uci.edu/ml/datasets/Heart+Disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "executionInfo": {
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     "timestamp": 1632358292004,
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   "outputs": [],
   "source": [
    "col_names = ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', \n",
    "             'thalach', 'exang', 'oldpeak', 'slope', 'ca', 'thal', 'num']\n",
    "df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/processed.cleveland.data', \n",
    "                 header = None, names = col_names)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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    "executionInfo": {
     "elapsed": 13,
     "status": "ok",
     "timestamp": 1632358292138,
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      "displayName": "Ilyas Ustun",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       303 non-null    float64\n",
      " 1   sex       303 non-null    float64\n",
      " 2   cp        303 non-null    float64\n",
      " 3   trestbps  303 non-null    float64\n",
      " 4   chol      303 non-null    float64\n",
      " 5   fbs       303 non-null    float64\n",
      " 6   restecg   303 non-null    float64\n",
      " 7   thalach   303 non-null    float64\n",
      " 8   exang     303 non-null    float64\n",
      " 9   oldpeak   303 non-null    float64\n",
      " 10  slope     303 non-null    float64\n",
      " 11  ca        303 non-null    object \n",
      " 12  thal      303 non-null    object \n",
      " 13  num       303 non-null    int64  \n",
      "dtypes: float64(11), int64(1), object(2)\n",
      "memory usage: 33.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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    },
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1632358292139,
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      "displayName": "Ilyas Ustun",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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     "elapsed": 9,
     "status": "ok",
     "timestamp": 1632358292140,
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      "displayName": "Ilyas Ustun",
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     },
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    },
    "id": "6EUiMETr8CX9",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 1, 3, 4], dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.num.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
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     "base_uri": "https://localhost:8080/"
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    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1632358292140,
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    },
    "id": "DV9SKB_wkkfz",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "num\n",
       "0    164\n",
       "1     55\n",
       "2     36\n",
       "3     35\n",
       "4     13\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.num.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1632358292141,
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    },
    "id": "go5lReYB8CX-"
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   "outputs": [],
   "source": [
    "df['num'] = np.where(df.num == 0, 0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
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     "timestamp": 1632358292274,
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    "id": "23nRUJyo8CX-",
    "outputId": "f7e8c32d-5cdc-4128-e0ca-4259d75a37cd"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.num.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "num\n",
       "0    164\n",
       "1    139\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.num.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
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    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1632358292642,
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    "id": "ZufT79rW8CX-",
    "outputId": "da525a0d-76bc-4db2-9eb7-f2a9aa519785"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0.0', '3.0', '2.0', '1.0', '?'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ca'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 130,
     "status": "ok",
     "timestamp": 1632358293209,
     "user": {
      "displayName": "Ilyas Ustun",
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    "id": "T167sZ1P8CX-",
    "outputId": "06c0c1c8-1b7e-465f-f6bc-f795661bb0b3"
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   "outputs": [],
   "source": [
    "df['thal'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "executionInfo": {
     "elapsed": 93,
     "status": "ok",
     "timestamp": 1632358294960,
     "user": {
      "displayName": "Ilyas Ustun",
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      "userId": "07102234121230289250"
     },
     "user_tz": 300
    },
    "id": "5PEfuAY58CX_"
   },
   "outputs": [],
   "source": [
    "df.replace('?', np.nan, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
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       "      <th>slope</th>\n",
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       "      <td>1.0</td>\n",
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       "      <td>233.0</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>229.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>130.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>41.0</td>\n",
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       "      <td>130.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>132.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>193.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>131.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>130.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0    63.0  1.0  1.0     145.0  233.0  1.0      2.0    150.0    0.0      2.3   \n",
       "1    67.0  1.0  4.0     160.0  286.0  0.0      2.0    108.0    1.0      1.5   \n",
       "2    67.0  1.0  4.0     120.0  229.0  0.0      2.0    129.0    1.0      2.6   \n",
       "3    37.0  1.0  3.0     130.0  250.0  0.0      0.0    187.0    0.0      3.5   \n",
       "4    41.0  0.0  2.0     130.0  204.0  0.0      2.0    172.0    0.0      1.4   \n",
       "..    ...  ...  ...       ...    ...  ...      ...      ...    ...      ...   \n",
       "298  45.0  1.0  1.0     110.0  264.0  0.0      0.0    132.0    0.0      1.2   \n",
       "299  68.0  1.0  4.0     144.0  193.0  1.0      0.0    141.0    0.0      3.4   \n",
       "300  57.0  1.0  4.0     130.0  131.0  0.0      0.0    115.0    1.0      1.2   \n",
       "301  57.0  0.0  2.0     130.0  236.0  0.0      2.0    174.0    0.0      0.0   \n",
       "302  38.0  1.0  3.0     138.0  175.0  0.0      0.0    173.0    0.0      0.0   \n",
       "\n",
       "     slope   ca thal  num  \n",
       "0      3.0  0.0  6.0    0  \n",
       "1      2.0  3.0  3.0    1  \n",
       "2      2.0  2.0  7.0    1  \n",
       "3      3.0  0.0  3.0    0  \n",
       "4      1.0  0.0  3.0    0  \n",
       "..     ...  ...  ...  ...  \n",
       "298    2.0  0.0  7.0    1  \n",
       "299    2.0  2.0  7.0    1  \n",
       "300    2.0  1.0  7.0    1  \n",
       "301    2.0  1.0  3.0    1  \n",
       "302    1.0  NaN  3.0    0  \n",
       "\n",
       "[303 rows x 14 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 117,
     "status": "ok",
     "timestamp": 1632358295834,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 300
    },
    "id": "0Pn6Fv0n8CX_",
    "outputId": "e23522df-57d1-4ccd-e697-be5bad1aa415"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0.0', '3.0', '2.0', '1.0', nan], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ca'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['6.0', '3.0', '7.0', nan], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['thal'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       303 non-null    float64\n",
      " 1   sex       303 non-null    float64\n",
      " 2   cp        303 non-null    float64\n",
      " 3   trestbps  303 non-null    float64\n",
      " 4   chol      303 non-null    float64\n",
      " 5   fbs       303 non-null    float64\n",
      " 6   restecg   303 non-null    float64\n",
      " 7   thalach   303 non-null    float64\n",
      " 8   exang     303 non-null    float64\n",
      " 9   oldpeak   303 non-null    float64\n",
      " 10  slope     303 non-null    float64\n",
      " 11  ca        299 non-null    object \n",
      " 12  thal      301 non-null    object \n",
      " 13  num       303 non-null    int32  \n",
      "dtypes: float64(11), int32(1), object(2)\n",
      "memory usage: 32.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "executionInfo": {
     "elapsed": 109,
     "status": "ok",
     "timestamp": 1632358296484,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 300
    },
    "id": "ySJztD_c8CX_"
   },
   "outputs": [],
   "source": [
    "df['ca'] = df.ca.astype(float)\n",
    "df['thal'] = df.thal.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 120,
     "status": "ok",
     "timestamp": 1632358297573,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 300
    },
    "id": "c_UsPhEJ8CYA",
    "outputId": "af4b13c1-7418-49ee-e9fd-05cd5f969271"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       303 non-null    float64\n",
      " 1   sex       303 non-null    float64\n",
      " 2   cp        303 non-null    float64\n",
      " 3   trestbps  303 non-null    float64\n",
      " 4   chol      303 non-null    float64\n",
      " 5   fbs       303 non-null    float64\n",
      " 6   restecg   303 non-null    float64\n",
      " 7   thalach   303 non-null    float64\n",
      " 8   exang     303 non-null    float64\n",
      " 9   oldpeak   303 non-null    float64\n",
      " 10  slope     303 non-null    float64\n",
      " 11  ca        299 non-null    float64\n",
      " 12  thal      301 non-null    float64\n",
      " 13  num       303 non-null    int32  \n",
      "dtypes: float64(13), int32(1)\n",
      "memory usage: 32.1 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1632358298402,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 300
    },
    "id": "1z0YDkVK8CYA",
    "outputId": "66a7c978-7182-4a47-e591-715cdbfd5b1a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "cp          0\n",
       "trestbps    0\n",
       "chol        0\n",
       "fbs         0\n",
       "restecg     0\n",
       "thalach     0\n",
       "exang       0\n",
       "oldpeak     0\n",
       "slope       0\n",
       "ca          4\n",
       "thal        2\n",
       "num         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_small = df[['age', 'cp', 'chol', 'num']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 842
    },
    "executionInfo": {
     "elapsed": 23146,
     "status": "error",
     "timestamp": 1632349718885,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 300
    },
    "id": "b5dtuBZB8CYB",
    "outputId": "10f21e71-9da3-4ed6-9634-78245f5fb3a2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1c81c053070>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 808.875x750 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g=sns.PairGrid(data = df_small.sample(100), diag_sharey = False, hue = 'num')\n",
    "g.map_diag(sns.kdeplot)\n",
    "g.map_upper(plt.scatter, alpha = 0.5)\n",
    "g.map_lower(sns.regplot,fit_reg = True, scatter_kws = {'alpha':0.3})\n",
    "\n",
    "g.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train-Test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "executionInfo": {
     "elapsed": 90,
     "status": "ok",
     "timestamp": 1632358341541,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 300
    },
    "id": "IzWsc5Ls8CYB"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>45.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>68.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>57.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0    63.0  1.0  1.0     145.0  233.0  1.0      2.0    150.0    0.0      2.3   \n",
       "1    67.0  1.0  4.0     160.0  286.0  0.0      2.0    108.0    1.0      1.5   \n",
       "2    67.0  1.0  4.0     120.0  229.0  0.0      2.0    129.0    1.0      2.6   \n",
       "3    37.0  1.0  3.0     130.0  250.0  0.0      0.0    187.0    0.0      3.5   \n",
       "4    41.0  0.0  2.0     130.0  204.0  0.0      2.0    172.0    0.0      1.4   \n",
       "..    ...  ...  ...       ...    ...  ...      ...      ...    ...      ...   \n",
       "298  45.0  1.0  1.0     110.0  264.0  0.0      0.0    132.0    0.0      1.2   \n",
       "299  68.0  1.0  4.0     144.0  193.0  1.0      0.0    141.0    0.0      3.4   \n",
       "300  57.0  1.0  4.0     130.0  131.0  0.0      0.0    115.0    1.0      1.2   \n",
       "301  57.0  0.0  2.0     130.0  236.0  0.0      2.0    174.0    0.0      0.0   \n",
       "302  38.0  1.0  3.0     138.0  175.0  0.0      0.0    173.0    0.0      0.0   \n",
       "\n",
       "     slope   ca  thal  \n",
       "0      3.0  0.0   6.0  \n",
       "1      2.0  3.0   3.0  \n",
       "2      2.0  2.0   7.0  \n",
       "3      3.0  0.0   3.0  \n",
       "4      1.0  0.0   3.0  \n",
       "..     ...  ...   ...  \n",
       "298    2.0  0.0   7.0  \n",
       "299    2.0  2.0   7.0  \n",
       "300    2.0  1.0   7.0  \n",
       "301    2.0  1.0   3.0  \n",
       "302    1.0  NaN   3.0  \n",
       "\n",
       "[303 rows x 13 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop('num', axis = 1)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "executionInfo": {
     "elapsed": 89,
     "status": "ok",
     "timestamp": 1632358352583,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 300
    },
    "id": "KbJjcdDz8CYB"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      1\n",
       "2      1\n",
       "3      0\n",
       "4      0\n",
       "      ..\n",
       "298    1\n",
       "299    1\n",
       "300    1\n",
       "301    1\n",
       "302    0\n",
       "Name: num, Length: 303, dtype: int32"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df.num\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "executionInfo": {
     "elapsed": 1,
     "status": "ok",
     "timestamp": 1632358356975,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 300
    },
    "id": "lqhcuXPY8CYB"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 20, stratify = y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 91,
     "status": "ok",
     "timestamp": 1632358358712,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 300
    },
    "id": "IEPD6Eyd8CYC",
    "outputId": "13a2ebd1-11ba-460a-f6da-46a2a6d3cc5d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "num\n",
       "0    0.541322\n",
       "1    0.458678\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "num\n",
       "0    0.540984\n",
       "1    0.459016\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T2_I6A158CYC"
   },
   "source": [
    "## Imputing NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "executionInfo": {
     "elapsed": 216,
     "status": "ok",
     "timestamp": 1632358381787,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 300
    },
    "id": "wuYejV-p8CYC"
   },
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "executionInfo": {
     "elapsed": 102,
     "status": "ok",
     "timestamp": 1632358383269,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 300
    },
    "id": "Gs6BWWOc8CYC"
   },
   "outputs": [],
   "source": [
    "imputer = SimpleImputer(strategy = 'most_frequent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 89,
     "status": "ok",
     "timestamp": 1632358391541,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 300
    },
    "id": "RqcSqB858CYC",
    "outputId": "81451ad0-cb06-4e56-9ec4-9b34b882b35e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "SimpleImputer(strategy='most_frequent')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "executionInfo": {
     "elapsed": 88,
     "status": "ok",
     "timestamp": 1632358394679,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 300
    },
    "id": "87kjxiH58CYD"
   },
   "outputs": [],
   "source": [
    "X_train = imputer.transform(X_train)\n",
    "X_test = imputer.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 101,
     "status": "ok",
     "timestamp": 1632358420554,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 300
    },
    "id": "SQNE8MQ38CYD",
    "outputId": "371dfd42-872e-427d-c4fd-019227a590c1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 242 entries, 0 to 241\n",
      "Data columns (total 13 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       242 non-null    float64\n",
      " 1   sex       242 non-null    float64\n",
      " 2   cp        242 non-null    float64\n",
      " 3   trestbps  242 non-null    float64\n",
      " 4   chol      242 non-null    float64\n",
      " 5   fbs       242 non-null    float64\n",
      " 6   restecg   242 non-null    float64\n",
      " 7   thalach   242 non-null    float64\n",
      " 8   exang     242 non-null    float64\n",
      " 9   oldpeak   242 non-null    float64\n",
      " 10  slope     242 non-null    float64\n",
      " 11  ca        242 non-null    float64\n",
      " 12  thal      242 non-null    float64\n",
      "dtypes: float64(13)\n",
      "memory usage: 24.7 KB\n"
     ]
    }
   ],
   "source": [
    "X_train = pd.DataFrame(X_train, columns = X.columns)\n",
    "X_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 95,
     "status": "ok",
     "timestamp": 1632358429433,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 300
    },
    "id": "7Y3z4TiImu6U",
    "outputId": "2c5214c2-8ac0-49be-c908-bd0dc472cfb9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 61 entries, 0 to 60\n",
      "Data columns (total 13 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       61 non-null     float64\n",
      " 1   sex       61 non-null     float64\n",
      " 2   cp        61 non-null     float64\n",
      " 3   trestbps  61 non-null     float64\n",
      " 4   chol      61 non-null     float64\n",
      " 5   fbs       61 non-null     float64\n",
      " 6   restecg   61 non-null     float64\n",
      " 7   thalach   61 non-null     float64\n",
      " 8   exang     61 non-null     float64\n",
      " 9   oldpeak   61 non-null     float64\n",
      " 10  slope     61 non-null     float64\n",
      " 11  ca        61 non-null     float64\n",
      " 12  thal      61 non-null     float64\n",
      "dtypes: float64(13)\n",
      "memory usage: 6.3 KB\n"
     ]
    }
   ],
   "source": [
    "X_test = pd.DataFrame(X_test, columns = X.columns)\n",
    "X_test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vMSV77pa8CYD"
   },
   "source": [
    "## One Hot Encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Dummy encoding using Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_dummy  = X_train.copy()\n",
    "X_test_dummy = X_test.copy()\n",
    "\n",
    "X_train_dummy['train_test'] = 'train'\n",
    "X_test_dummy['train_test'] = 'test'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_combi = pd.concat([X_train_dummy, X_test_dummy], axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_combi = pd.get_dummies(data = X_combi, columns = ['cp', 'restecg', 'slope', 'thal'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>ca</th>\n",
       "      <th>train_test</th>\n",
       "      <th>...</th>\n",
       "      <th>cp_4.0</th>\n",
       "      <th>restecg_0.0</th>\n",
       "      <th>restecg_1.0</th>\n",
       "      <th>restecg_2.0</th>\n",
       "      <th>slope_1.0</th>\n",
       "      <th>slope_2.0</th>\n",
       "      <th>slope_3.0</th>\n",
       "      <th>thal_3.0</th>\n",
       "      <th>thal_6.0</th>\n",
       "      <th>thal_7.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>train</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>train</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>train</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>43.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>train</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>train</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  sex  trestbps   chol  fbs  thalach  exang  oldpeak   ca train_test  \\\n",
       "0  37.0  0.0     120.0  215.0  0.0    170.0    0.0      0.0  0.0      train   \n",
       "1  57.0  0.0     140.0  241.0  0.0    123.0    1.0      0.2  0.0      train   \n",
       "2  69.0  1.0     160.0  234.0  1.0    131.0    0.0      0.1  1.0      train   \n",
       "3  43.0  1.0     120.0  177.0  0.0    120.0    1.0      2.5  0.0      train   \n",
       "4  58.0  0.0     130.0  197.0  0.0    131.0    0.0      0.6  0.0      train   \n",
       "\n",
       "   ...  cp_4.0  restecg_0.0  restecg_1.0  restecg_2.0  slope_1.0  slope_2.0  \\\n",
       "0  ...   False         True        False        False       True      False   \n",
       "1  ...    True         True        False        False      False       True   \n",
       "2  ...   False        False        False         True      False       True   \n",
       "3  ...    True        False        False         True      False       True   \n",
       "4  ...    True         True        False        False      False       True   \n",
       "\n",
       "   slope_3.0  thal_3.0  thal_6.0  thal_7.0  \n",
       "0      False      True     False     False  \n",
       "1      False     False     False      True  \n",
       "2      False      True     False     False  \n",
       "3      False     False     False      True  \n",
       "4      False      True     False     False  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_combi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_dummy = X_combi.loc[X_combi['train_test'] == 'train', :]\n",
    "X_test_dummy = X_combi.loc[X_combi['train_test'] == 'test', :]\n",
    "\n",
    "X_train_dummy = X_train_dummy.drop('train_test', axis = 1)\n",
    "X_test_dummy = X_test_dummy.drop('train_test', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'sex', 'trestbps', 'chol', 'fbs', 'thalach', 'exang', 'oldpeak',\n",
       "       'ca', 'cp_1.0', 'cp_2.0', 'cp_3.0', 'cp_4.0', 'restecg_0.0',\n",
       "       'restecg_1.0', 'restecg_2.0', 'slope_1.0', 'slope_2.0', 'slope_3.0',\n",
       "       'thal_3.0', 'thal_6.0', 'thal_7.0'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_dummy.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Onehot encoding using variable names in sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "executionInfo": {
     "elapsed": 88,
     "status": "ok",
     "timestamp": 1632358540414,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 300
    },
    "id": "C2HhQTCZ8CYD"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.compose import make_column_transformer, ColumnTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "ohe = OneHotEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 91,
     "status": "ok",
     "timestamp": 1632358542680,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 300
    },
    "id": "iFaLSPeZ8CYD",
    "outputId": "84f066b4-6354-4ea9-ecf7-227720ece921"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n",
       "       'exang', 'oldpeak', 'slope', 'ca', 'thal', 'num'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "id": "9QFewUZ8kHpf",
    "outputId": "753be17c-4cc4-4dff-b909-5fb72b548b43"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
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       "      <th>num</th>\n",
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       "      <td>67.0</td>\n",
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       "      <td>2.6</td>\n",
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       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0  63.0  1.0  1.0     145.0  233.0  1.0      2.0    150.0    0.0      2.3   \n",
       "1  67.0  1.0  4.0     160.0  286.0  0.0      2.0    108.0    1.0      1.5   \n",
       "2  67.0  1.0  4.0     120.0  229.0  0.0      2.0    129.0    1.0      2.6   \n",
       "3  37.0  1.0  3.0     130.0  250.0  0.0      0.0    187.0    0.0      3.5   \n",
       "4  41.0  0.0  2.0     130.0  204.0  0.0      2.0    172.0    0.0      1.4   \n",
       "\n",
       "   slope   ca  thal  num  \n",
       "0    3.0  0.0   6.0    0  \n",
       "1    2.0  3.0   3.0    1  \n",
       "2    2.0  2.0   7.0    1  \n",
       "3    3.0  0.0   3.0    0  \n",
       "4    1.0  0.0   3.0    0  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "ct = ColumnTransformer(\n",
    "    transformers=[('ohe', ohe, ['cp', 'restecg', 'slope', 'thal'])], \n",
    "    remainder=\"passthrough\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
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       "      <td>58.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
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       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>56.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>44.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>59.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>221.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>58.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>283.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>242 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0    37.0  0.0  3.0     120.0  215.0  0.0      0.0    170.0    0.0      0.0   \n",
       "1    57.0  0.0  4.0     140.0  241.0  0.0      0.0    123.0    1.0      0.2   \n",
       "2    69.0  1.0  1.0     160.0  234.0  1.0      2.0    131.0    0.0      0.1   \n",
       "3    43.0  1.0  4.0     120.0  177.0  0.0      2.0    120.0    1.0      2.5   \n",
       "4    58.0  0.0  4.0     130.0  197.0  0.0      0.0    131.0    0.0      0.6   \n",
       "..    ...  ...  ...       ...    ...  ...      ...      ...    ...      ...   \n",
       "237  58.0  0.0  4.0     170.0  225.0  1.0      2.0    146.0    1.0      2.8   \n",
       "238  56.0  1.0  1.0     120.0  193.0  0.0      2.0    162.0    0.0      1.9   \n",
       "239  44.0  1.0  4.0     112.0  290.0  0.0      2.0    153.0    0.0      0.0   \n",
       "240  59.0  1.0  2.0     140.0  221.0  0.0      0.0    164.0    1.0      0.0   \n",
       "241  58.0  0.0  1.0     150.0  283.0  1.0      2.0    162.0    0.0      1.0   \n",
       "\n",
       "     slope   ca  thal  \n",
       "0      1.0  0.0   3.0  \n",
       "1      2.0  0.0   7.0  \n",
       "2      2.0  1.0   3.0  \n",
       "3      2.0  0.0   7.0  \n",
       "4      2.0  0.0   3.0  \n",
       "..     ...  ...   ...  \n",
       "237    2.0  2.0   6.0  \n",
       "238    2.0  0.0   7.0  \n",
       "239    1.0  1.0   3.0  \n",
       "240    1.0  0.0   3.0  \n",
       "241    1.0  0.0   3.0  \n",
       "\n",
       "[242 rows x 13 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 91,
     "status": "ok",
     "timestamp": 1632358743041,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 300
    },
    "id": "4_T8Ccz-8CYE",
    "outputId": "718123db-f4d3-4ce8-f653-f957fa76ae27"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;ohe&#x27;, OneHotEncoder(),\n",
       "                                 [&#x27;cp&#x27;, &#x27;restecg&#x27;, &#x27;slope&#x27;, &#x27;thal&#x27;])])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for ColumnTransformer</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;ohe&#x27;, OneHotEncoder(),\n",
       "                                 [&#x27;cp&#x27;, &#x27;restecg&#x27;, &#x27;slope&#x27;, &#x27;thal&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">ohe</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;cp&#x27;, &#x27;restecg&#x27;, &#x27;slope&#x27;, &#x27;thal&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder()</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">remainder</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;age&#x27;, &#x27;sex&#x27;, &#x27;trestbps&#x27;, &#x27;chol&#x27;, &#x27;fbs&#x27;, &#x27;thalach&#x27;, &#x27;exang&#x27;, &#x27;oldpeak&#x27;, &#x27;ca&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">passthrough</label><div class=\"sk-toggleable__content fitted\"><pre>passthrough</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "ColumnTransformer(remainder='passthrough',\n",
       "                  transformers=[('ohe', OneHotEncoder(),\n",
       "                                 ['cp', 'restecg', 'slope', 'thal'])])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 99,
     "status": "ok",
     "timestamp": 1632358747316,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 300
    },
    "id": "Z_Lz64Ho8CYE",
    "outputId": "13757319-d56f-4726-ad92-884f495ea33f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(242, 22)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct.transform(X_train).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "executionInfo": {
     "elapsed": 83,
     "status": "ok",
     "timestamp": 1632358823016,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 300
    },
    "id": "5OtD3Iqr8CYF"
   },
   "outputs": [],
   "source": [
    "X_train2 = ct.transform(X_train)\n",
    "X_test2 = ct.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ohe__cp_1.0', 'ohe__cp_2.0', 'ohe__cp_3.0', 'ohe__cp_4.0',\n",
       "       'ohe__restecg_0.0', 'ohe__restecg_1.0', 'ohe__restecg_2.0',\n",
       "       'ohe__slope_1.0', 'ohe__slope_2.0', 'ohe__slope_3.0',\n",
       "       'ohe__thal_3.0', 'ohe__thal_6.0', 'ohe__thal_7.0',\n",
       "       'remainder__age', 'remainder__sex', 'remainder__trestbps',\n",
       "       'remainder__chol', 'remainder__fbs', 'remainder__thalach',\n",
       "       'remainder__exang', 'remainder__oldpeak', 'remainder__ca'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct.get_feature_names_out()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 89,
     "status": "ok",
     "timestamp": 1632358840674,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 300
    },
    "id": "GuQ3jTOk8CYF",
    "outputId": "e1128eb6-ccb1-49cd-e19d-d1fa8217229e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(242, 22)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 90,
     "status": "ok",
     "timestamp": 1632358842190,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 300
    },
    "id": "ErWWtZNrkHpq",
    "outputId": "e61cf345-df15-4d46-bd21-74a2d0aaafc9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(61, 22)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnosing bias and variance problems - Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimize the C value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 106,
     "status": "ok",
     "timestamp": 1632360751409,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 300
    },
    "id": "mvYKgIL98CYL",
    "outputId": "af3bb2a8-2701-4602-9ad8-e90de3fe3e41"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0001,\n",
       " 0.001,\n",
       " 0.01,\n",
       " 0.03,\n",
       " 0.1,\n",
       " 0.3,\n",
       " 1,\n",
       " 2,\n",
       " 5,\n",
       " 10,\n",
       " 15,\n",
       " 20,\n",
       " 50,\n",
       " 100,\n",
       " 1000,\n",
       " 10000,\n",
       " 100000]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C_vals = [0.0001, 0.001, 0.01, 0.03, 0.1, 0.3, 1, 2, 5, 10, 15, 20, 50, 100, 1000, 10000, 100000]\n",
    "C_vals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create Train - Validation Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_val, y_train, y_val = train_test_split(X_train2, y_train, test_size = 0.20, random_state = 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MBidCy46kHp8"
   },
   "source": [
    "### Validation Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2952,
     "status": "ok",
     "timestamp": 1632360913085,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 300
    },
    "id": "tn6xXQMf8CYO",
    "outputId": "b793d85f-cb4d-4582-8a0b-0fc41fb9ec9b"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "\n",
    "scores_tr = []\n",
    "scores_val = []\n",
    "\n",
    "for C in C_vals:\n",
    "    clf = LogisticRegression(C = C, max_iter = 2000)\n",
    "    clf.fit(X_train, y_train)\n",
    "\n",
    "    pred = clf.predict(X_train)\n",
    "    scores_tr.append(metrics.accuracy_score(y_train, pred))\n",
    "    \n",
    "    pred = clf.predict(X_val)\n",
    "    scores_val.append(metrics.accuracy_score(y_val, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.689119170984456,\n",
       " 0.6994818652849741,\n",
       " 0.7875647668393783,\n",
       " 0.8186528497409327,\n",
       " 0.8549222797927462,\n",
       " 0.8704663212435233,\n",
       " 0.8756476683937824,\n",
       " 0.8704663212435233,\n",
       " 0.8704663212435233,\n",
       " 0.8704663212435233,\n",
       " 0.8704663212435233,\n",
       " 0.8704663212435233,\n",
       " 0.8704663212435233,\n",
       " 0.8704663212435233,\n",
       " 0.8704663212435233,\n",
       " 0.8704663212435233,\n",
       " 0.8756476683937824]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores_tr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6326530612244898,\n",
       " 0.6938775510204082,\n",
       " 0.7346938775510204,\n",
       " 0.7551020408163265,\n",
       " 0.7755102040816326,\n",
       " 0.7755102040816326,\n",
       " 0.7755102040816326,\n",
       " 0.7755102040816326,\n",
       " 0.7755102040816326,\n",
       " 0.7755102040816326,\n",
       " 0.7755102040816326,\n",
       " 0.7755102040816326,\n",
       " 0.7755102040816326,\n",
       " 0.7755102040816326,\n",
       " 0.7755102040816326,\n",
       " 0.7755102040816326,\n",
       " 0.7755102040816326]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 644
    },
    "executionInfo": {
     "elapsed": 439,
     "status": "ok",
     "timestamp": 1632360914738,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 300
    },
    "id": "SbVnq3oD8CYP",
    "outputId": "ad242189-41fc-4fe1-a425-53d0248b5869"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(dpi = 150)\n",
    "plt.plot(scores_tr, marker = 'o', color = 'blue', label = 'train')\n",
    "plt.plot(scores_val, color = 'orange', marker = 'o', label = 'val')\n",
    "plt.title('Validation Curve')\n",
    "plt.xticks(ticks = np.arange(len(C_vals)), labels = C_vals, rotation = 45)\n",
    "plt.xlabel('C')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S65kEIFA8CYK"
   },
   "source": [
    "### Learning Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "executionInfo": {
     "elapsed": 3363,
     "status": "ok",
     "timestamp": 1632361061419,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 300
    },
    "id": "QHe5w9XEkHqF"
   },
   "outputs": [],
   "source": [
    "accuracy_train = []\n",
    "accuracy_val = []\n",
    "clf = LogisticRegression(max_iter = 1000, C = 1, verbose = 0)\n",
    "\n",
    "num_points = list(range(5, X_train.shape[0], 10))\n",
    "\n",
    "for n in num_points:\n",
    "    clf.fit(X_train[0:n, :], y_train[0:n])\n",
    "    pred_trn = clf.predict(X_train[0:n, :])\n",
    "    accuracy_train.append(metrics.accuracy_score(y_true = y_train[0:n], y_pred = pred_trn))\n",
    "    pred_val = clf.predict(X_val)\n",
    "    accuracy_val.append(metrics.accuracy_score(y_true = y_val, y_pred = pred_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 594
    },
    "executionInfo": {
     "elapsed": 443,
     "status": "ok",
     "timestamp": 1632361064887,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 300
    },
    "id": "2I-IZWI-kHqG",
    "outputId": "4de1e3e6-3227-4221-8207-601a18c4fce1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(dpi = 150)\n",
    "ax.plot(num_points, accuracy_train, marker = 'o', color = 'blue', label = 'Train')\n",
    "ax.plot(num_points, accuracy_val, marker = 'o', color = 'orange', label = 'Val')\n",
    "ax.set_xlabel('Number of points in training set')\n",
    "ax.set_ylabel('Accuracy')\n",
    "ax.set_title(\"Learning Curve\")\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "id": "ocbDQlX4kHqI"
   },
   "outputs": [],
   "source": [
    "# decreasing the interval length for smoother curves\n",
    "accuracy_train = []\n",
    "accuracy_val = []\n",
    "clf = LogisticRegression(max_iter = 1000, solver = 'lbfgs', penalty = 'l2', C = 1, verbose = 0)\n",
    "\n",
    "num_points = list(range(5, X_train.shape[0], 5))\n",
    "\n",
    "for n in num_points:\n",
    "    clf.fit(X_train[0:n, :], y_train[0:n])\n",
    "    pred_trn = clf.predict(X_train[0:n, :])\n",
    "    accuracy_train.append(metrics.accuracy_score(y_true = y_train[0:n], y_pred = pred_trn))\n",
    "    pred_val = clf.predict(X_val)\n",
    "    accuracy_val.append(metrics.accuracy_score(y_true = y_val, y_pred = pred_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "id": "XmJ2A4E9kHqJ",
    "outputId": "32b7a892-15a8-4409-f0d8-9e3eda8892a9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(dpi = 150)\n",
    "ax.plot(num_points, accuracy_train, marker = 'o', color = 'blue', label = 'Train')\n",
    "ax.plot(num_points, accuracy_val, marker = 'o', color = 'orange', label = 'Val')\n",
    "ax.set_xlabel('Number of points in training')\n",
    "ax.set_ylabel('Accuracy')\n",
    "ax.set_title(\"Learning Curve\")\n",
    "ax.legend();\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnosing bias and variance problems - Support Vector Machine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Validation Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "id": "pQjnLjjpkHqN"
   },
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "from sklearn import metrics\n",
    "\n",
    "scores_tr2 = []\n",
    "scores_val2 = []\n",
    "\n",
    "for C in C_vals:\n",
    "    clf2 = svm.SVC(C = C, max_iter = 2000)\n",
    "    clf2.fit(X_train, y_train)\n",
    "\n",
    "    pred = clf2.predict(X_train)\n",
    "    scores_tr2.append(metrics.accuracy_score(y_train, pred))\n",
    "    \n",
    "    pred = clf2.predict(X_val)\n",
    "    scores_val2.append(metrics.accuracy_score(y_val, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(dpi = 150)\n",
    "plt.plot(scores_tr2, marker = 'o', color = 'blue', label = 'train')\n",
    "plt.plot(scores_val2, color = 'orange', marker = 'o', label = 'val')\n",
    "plt.title('Validation Curve')\n",
    "plt.xticks(ticks = np.arange(len(C_vals)), labels = C_vals, rotation = 45)\n",
    "plt.xlabel('C')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Learning Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_train2 = []\n",
    "accuracy_val2 = []\n",
    "clf2 = svm.SVC(max_iter = 1000, C = 10, verbose = 0)\n",
    "\n",
    "num_points = list(range(5, X_train.shape[0], 5))\n",
    "\n",
    "for n in num_points:\n",
    "    clf2.fit(X_train[0:n, :], y_train[0:n])\n",
    "    pred_trn = clf2.predict(X_train[0:n, :])\n",
    "    accuracy_train2.append(metrics.accuracy_score(y_true = y_train[0:n], y_pred = pred_trn))\n",
    "    pred_val = clf2.predict(X_val)\n",
    "    accuracy_val2.append(metrics.accuracy_score(y_true = y_val, y_pred = pred_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(dpi = 150)\n",
    "ax.plot(num_points, accuracy_train2, marker = 'o', color = 'blue', label = 'Train')\n",
    "ax.plot(num_points, accuracy_val2, marker = 'o', color = 'orange', label = 'Val')\n",
    "ax.set_xlabel('Number of points in training')\n",
    "ax.set_ylabel('Accuracy')\n",
    "ax.set_title(\"Learning Curve\")\n",
    "ax.legend();\n",
    "ax.grid()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [
    "eeqQ5iLu8CYN",
    "MBidCy46kHp8",
    "S65kEIFA8CYK"
   ],
   "name": "model evaluation and debugging.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.11"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": false,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": false,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}