7. Cross-validation and Grid Search.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "# Lecture 7: Supervised Learning - Decision Tree, Cross-validation and Grid Search\n",
    "Instructor: Md Shahidullah Kawsar\n",
    "<br>Data Scientist, IDARE, Houston, TX, USA\n",
    "\n",
    "#### Objectives:\n",
    "- Decision Tree Classification\n",
    "- Cross-Validation\n",
    "- Grid Search\n",
    "\n",
    "#### References:\n",
    "[1] Decision and Classification Trees, Clearly Explained!!! https://www.youtube.com/watch?v=_L39rN6gz7Y&ab_channel=StatQuestwithJoshStarmer\n",
    "<br>[2] StatQuest: Decision Trees: https://www.youtube.com/watch?v=7VeUPuFGJHk&t=302s&ab_channel=StatQuestwithJoshStarmer\n",
    "<br>[3] StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Data: https://www.youtube.com/watch?v=wpNl-JwwplA&ab_channel=StatQuestwithJoshStarmer\n",
    "<br>[4] scikit-learn decision tree classifier: https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html\n",
    "<br>[5] Decision Trees in Python from Start to Finish: https://www.youtube.com/watch?v=q90UDEgYqeI&ab_channel=StatQuestwithJoshStarmer\n",
    "<br> [6] Plotting a tree diagram https://scikit-learn.org/stable/modules/generated/sklearn.tree.plot_tree.html\n",
    "<br>[7] Plotting confusion matrix: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.plot_confusion_matrix.html\n",
    "<br>[8] Regression Trees, Clearly Explained!!! https://www.youtube.com/watch?v=g9c66TUylZ4&ab_channel=StatQuestwithJoshStarmer\n",
    "<br>[9] Machine Learning Fundamentals: Cross Validation: https://www.youtube.com/watch?v=fSytzGwwBVw&ab_channel=StatQuestwithJoshStarmer\n",
    "<br>[10] Machine Learning Tutorial Python - 16: Hyper parameter Tuning (GridSearchCV): https://www.youtube.com/watch?v=HdlDYng8g9s&ab_channel=codebasics\n",
    "<br>[11] Data Source: https://archive.ics.uci.edu/ml/datasets/heart+disease\n",
    "<br>[12] https://medium.com/analytics-steps/understanding-the-gini-index-and-information-gain-in-decision-trees-ab4720518ba8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**age:** age in years\n",
    "<br>**sex:** sex (1 = male; 0 = female)\n",
    "<br>**cp:** chest pain type\n",
    "- 1 = typical angina\n",
    "- 2 = atypical angina\n",
    "- 3 = non-anginal pain\n",
    "- 4 = asymptomatic\n",
    "\n",
    "<br>**trestbps:** resting blood pressure (in mm Hg on admission to the hospital)\n",
    "<br>**chol:** serum cholestoral in mg/dl\n",
    "<br>**fbs:** (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)\n",
    "<br>**restecg:** resting electrocardiographic results\n",
    "- 0 = normal\n",
    "- 1 = having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV)\n",
    "- 2 = showing probable or definite left ventricular hypertrophy by Estes' criteria\n",
    "\n",
    "<br>**thalach:** maximum heart rate achieved\n",
    "<br>**exang:** exercise induced angina (1 = yes; 0 = no)\n",
    "<br>**oldpeak:** ST depression induced by exercise relative to rest\n",
    "<br>**slope:** the slope of the peak exercise ST segment\n",
    "- 1 = upsloping\n",
    "- 2 = flat\n",
    "- 3 = downsloping\n",
    "\n",
    "<br>**ca:** number of major vessels (0-3) colored by flourosopy\n",
    "<br>**thal:** 3 = normal; 6 = fixed defect; 7 = reversable defect\n",
    "<br>**hd:** heart disease "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.tree import plot_tree\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "from sklearn.metrics import roc_curve, roc_auc_score, precision_score, recall_score, f1_score\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading heart disease data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
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       "      <td>2.0</td>\n",
       "      <td>172.0</td>\n",
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      "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",
       "5  56.0  1.0  2.0     120.0  236.0  0.0      0.0    178.0    0.0      0.8   \n",
       "6  62.0  0.0  4.0     140.0  268.0  0.0      2.0    160.0    0.0      3.6   \n",
       "7  57.0  0.0  4.0     120.0  354.0  0.0      0.0    163.0    1.0      0.6   \n",
       "8  63.0  1.0  4.0     130.0  254.0  0.0      2.0    147.0    0.0      1.4   \n",
       "9  53.0  1.0  4.0     140.0  203.0  1.0      2.0    155.0    1.0      3.1   \n",
       "\n",
       "   slope   ca  thal  hd  \n",
       "0    3.0  0.0   6.0   0  \n",
       "1    2.0  3.0   3.0   2  \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",
       "5    1.0  0.0   3.0   0  \n",
       "6    3.0  2.0   3.0   3  \n",
       "7    1.0  0.0   3.0   0  \n",
       "8    2.0  1.0   7.0   2  \n",
       "9    3.0  0.0   7.0   1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 297 entries, 0 to 301\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       297 non-null    float64\n",
      " 1   sex       297 non-null    float64\n",
      " 2   cp        297 non-null    float64\n",
      " 3   trestbps  297 non-null    float64\n",
      " 4   chol      297 non-null    float64\n",
      " 5   fbs       297 non-null    float64\n",
      " 6   restecg   297 non-null    float64\n",
      " 7   thalach   297 non-null    float64\n",
      " 8   exang     297 non-null    float64\n",
      " 9   oldpeak   297 non-null    float64\n",
      " 10  slope     297 non-null    float64\n",
      " 11  ca        297 non-null    float64\n",
      " 12  thal      297 non-null    float64\n",
      " 13  hd        297 non-null    int64  \n",
      "dtypes: float64(13), int64(1)\n",
      "memory usage: 34.8 KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"processed.cleveland.data\", header=None, na_values=\"?\")\n",
    "\n",
    "df = df.dropna()\n",
    "# print(df.columns)\n",
    "\n",
    "df.columns = ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', \n",
    "              'restecg', 'thalach', 'exang', 'oldpeak', \n",
    "              'slope', 'ca', 'thal', 'hd']\n",
    "\n",
    "display(df.head(10))\n",
    "print(df.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0    164\n",
       "7.0    115\n",
       "6.0     18\n",
       "Name: thal, dtype: int64"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"thal\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    174\n",
       "1.0     65\n",
       "2.0     38\n",
       "3.0     20\n",
       "Name: ca, dtype: int64"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"ca\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0    142\n",
       "3.0     83\n",
       "2.0     49\n",
       "1.0     23\n",
       "Name: cp, dtype: int64"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"cp\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    139\n",
       "2.0    137\n",
       "3.0     21\n",
       "Name: slope, dtype: int64"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"slope\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    147\n",
       "2.0    146\n",
       "1.0      4\n",
       "Name: restecg, dtype: int64"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"restecg\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_vars = [\"restecg\", \"slope\", \"cp\", \"ca\", \"thal\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Target variable: 'hd'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    160\n",
       "1     54\n",
       "2     35\n",
       "3     35\n",
       "4     13\n",
       "Name: hd, dtype: int64"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"hd\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[df[\"hd\"]>1] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    160\n",
       "1    137\n",
       "Name: hd, dtype: int64"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"hd\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create feature set and target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(297, 13) (297, 1)\n",
      "Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n",
      "       'exang', 'oldpeak', 'slope', 'ca', 'thal'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "X = df.drop(\"hd\", axis=1)\n",
    "y = df[[\"hd\"]]\n",
    "\n",
    "print(X.shape, y.shape)\n",
    "print(X.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### One-hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(297, 21)\n"
     ]
    }
   ],
   "source": [
    "X = pd.get_dummies(X, columns=cat_vars, drop_first=True)\n",
    "\n",
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'sex', 'trestbps', 'chol', 'fbs', 'thalach', 'exang', 'oldpeak',\n",
       "       'restecg_1.0', 'restecg_2.0', 'slope_2.0', 'slope_3.0', 'cp_2.0',\n",
       "       'cp_3.0', 'cp_4.0', 'ca_1.0', 'ca_2.0', 'ca_3.0', 'thal_3.0',\n",
       "       'thal_6.0', 'thal_7.0'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "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>restecg_1.0</th>\n",
       "      <th>restecg_2.0</th>\n",
       "      <th>...</th>\n",
       "      <th>slope_3.0</th>\n",
       "      <th>cp_2.0</th>\n",
       "      <th>cp_3.0</th>\n",
       "      <th>cp_4.0</th>\n",
       "      <th>ca_1.0</th>\n",
       "      <th>ca_2.0</th>\n",
       "      <th>ca_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>63.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  sex  trestbps   chol  fbs  thalach  exang  oldpeak  restecg_1.0  \\\n",
       "0  63.0  1.0     145.0  233.0  1.0    150.0    0.0      2.3            0   \n",
       "1   1.0  1.0       1.0    1.0  1.0      1.0    1.0      1.0            1   \n",
       "2  67.0  1.0     120.0  229.0  0.0    129.0    1.0      2.6            0   \n",
       "3  37.0  1.0     130.0  250.0  0.0    187.0    0.0      3.5            0   \n",
       "4  41.0  0.0     130.0  204.0  0.0    172.0    0.0      1.4            0   \n",
       "\n",
       "   restecg_2.0  ...  slope_3.0  cp_2.0  cp_3.0  cp_4.0  ca_1.0  ca_2.0  \\\n",
       "0            1  ...          1       0       0       0       0       0   \n",
       "1            0  ...          0       0       0       0       1       0   \n",
       "2            1  ...          0       0       0       1       0       1   \n",
       "3            0  ...          1       0       1       0       0       0   \n",
       "4            1  ...          0       1       0       0       0       0   \n",
       "\n",
       "   ca_3.0  thal_3.0  thal_6.0  thal_7.0  \n",
       "0       0         0         1         0  \n",
       "1       0         0         0         0  \n",
       "2       0         0         0         1  \n",
       "3       0         1         0         0  \n",
       "4       0         1         0         0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(X.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Split the dataset intro train and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(237, 21) (60, 21) (237, 1) (60, 1)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, \n",
    "                                                    y, \n",
    "                                                    test_size=0.2, \n",
    "                                                    random_state=42, \n",
    "                                                    stratify=y)\n",
    "\n",
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Decision Tree classifier, no grid search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_DT = DecisionTreeClassifier()\n",
    "# model_DT = LogisticRegression()\n",
    "model_DT = model_DT.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tree Diagram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.figure(figsize = (15, 15))\n",
    "# plot_tree(model_DT, \n",
    "#           filled=True,\n",
    "#           rounded=True,\n",
    "#           class_names = [\"No HD\", \"Yes HD\"],\n",
    "#           feature_names = X.columns,\n",
    "#           max_depth=2, \n",
    "#           fontsize=15)\n",
    "\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Performance Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(model_DT, \n",
    "                      X_test, \n",
    "                      y_test, \n",
    "                      display_labels=[\"Doesn't have HD\", \"Has HD\"],\n",
    "                      cmap='Greys')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_DT = model_DT.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.85\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.84      0.86        32\n",
      "           1       0.83      0.86      0.84        28\n",
      "\n",
      "    accuracy                           0.85        60\n",
      "   macro avg       0.85      0.85      0.85        60\n",
      "weighted avg       0.85      0.85      0.85        60\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Accuracy: ', accuracy_score(y_test, y_pred_DT))\n",
    "\n",
    "print(classification_report(y_test, y_pred_DT))\n",
    "\n",
    "fpr, tpr, thr = roc_curve(y_test, model_DT.predict_proba(X_test)[:,1])\n",
    "auc = roc_auc_score(y_test, y_pred_DT)\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.plot(fpr, tpr, color='green', lw=2, label=\"Curve Area = %0.3f\" % auc)\n",
    "plt.plot([0, 1], [0, 1], color='blue', lw=2, linestyle='--')\n",
    "plt.xlabel('False Positive Rate (FPR)')\n",
    "plt.ylabel('True Positive Rate (TPR)')\n",
    "plt.title('ROC curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Grid Search and cross-validation\n",
    "\n",
    "![image-2.png](attachment:image-2.png)\n",
    "image source: https://www.analyticsvidhya.com/blog/2021/05/importance-of-cross-validation-are-evaluation-metrics-enough/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {'criterion': ['gini','entropy'],\n",
    "              'max_depth': [3, 4, 5, 6, 7, None],\n",
    "              'max_features': [\"auto\"]}\n",
    "\n",
    "model = DecisionTreeClassifier(random_state=42)\n",
    "grid_search = GridSearchCV(estimator=model,\n",
    "                           param_grid=parameters,\n",
    "                           scoring=\"recall\",\n",
    "                           cv=5,\n",
    "                           n_jobs=-1)\n",
    "\n",
    "# grid_search = GridSearchCV(estimator=model,\n",
    "#                            param_grid=parameters,\n",
    "#                            scoring=\"accuracy\",\n",
    "#                            cv=5,\n",
    "#                            n_jobs=-1)\n",
    "\n",
    "# grid_search = GridSearchCV(estimator=model,\n",
    "#                            param_grid=parameters,\n",
    "#                            scoring=\"precision\",\n",
    "#                            cv=5,\n",
    "#                            n_jobs=-1)\n",
    "\n",
    "# fit the model with the best hyper-parameters\n",
    "grid_result = grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### hyperparameters for grid search in each iteration\n",
    "- iteration 1: criterion='gini', max_depth=3\n",
    "- iteration 2: criterion='gini', max_depth=4\n",
    "- iteration 3: criterion='gini', max_depth=5\n",
    "- iteration 4: criterion='gini', max_depth=6\n",
    "- iteration 5: criterion='gini', max_depth=7\n",
    "- iteration 6: criterion='entropy', max_depth=3\n",
    "- iteration 7: criterion='entropy', max_depth=4\n",
    "- iteration 8: criterion='entropy', max_depth=5\n",
    "- iteration 9: criterion='entropy', max_depth=6\n",
    "- iteration 10: criterion='entropy', max_depth=7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = grid_result.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best hyperparameters in the grid search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'entropy', 'max_depth': None, 'max_features': 'auto'}"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_result.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "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>param_criterion</th>\n",
       "      <th>param_max_depth</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>entropy</td>\n",
       "      <td>None</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.863636</td>\n",
       "      <td>0.863636</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.787879</td>\n",
       "      <td>0.112978</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.863636</td>\n",
       "      <td>0.772727</td>\n",
       "      <td>0.681818</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.769264</td>\n",
       "      <td>0.108334</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gini</td>\n",
       "      <td>6</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.863636</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.681818</td>\n",
       "      <td>0.523810</td>\n",
       "      <td>0.759307</td>\n",
       "      <td>0.140178</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>gini</td>\n",
       "      <td>5</td>\n",
       "      <td>0.863636</td>\n",
       "      <td>0.863636</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.751082</td>\n",
       "      <td>0.120771</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gini</td>\n",
       "      <td>7</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.863636</td>\n",
       "      <td>0.772727</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.751082</td>\n",
       "      <td>0.127430</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>entropy</td>\n",
       "      <td>6</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.750649</td>\n",
       "      <td>0.126021</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>entropy</td>\n",
       "      <td>7</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.523810</td>\n",
       "      <td>0.750216</td>\n",
       "      <td>0.162321</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gini</td>\n",
       "      <td>3</td>\n",
       "      <td>0.863636</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.772727</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.722078</td>\n",
       "      <td>0.173822</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>entropy</td>\n",
       "      <td>3</td>\n",
       "      <td>0.863636</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.772727</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.476190</td>\n",
       "      <td>0.713420</td>\n",
       "      <td>0.140906</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>entropy</td>\n",
       "      <td>4</td>\n",
       "      <td>0.863636</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.476190</td>\n",
       "      <td>0.704329</td>\n",
       "      <td>0.160370</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>gini</td>\n",
       "      <td>4</td>\n",
       "      <td>0.863636</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.772727</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.476190</td>\n",
       "      <td>0.695238</td>\n",
       "      <td>0.154851</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>entropy</td>\n",
       "      <td>5</td>\n",
       "      <td>0.863636</td>\n",
       "      <td>0.772727</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.476190</td>\n",
       "      <td>0.686147</td>\n",
       "      <td>0.136998</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   param_criterion param_max_depth  split0_test_score  split1_test_score  \\\n",
       "11         entropy            None           0.909091           0.863636   \n",
       "5             gini            None           0.909091           0.863636   \n",
       "3             gini               6           0.909091           0.863636   \n",
       "2             gini               5           0.863636           0.863636   \n",
       "4             gini               7           0.909091           0.863636   \n",
       "9          entropy               6           0.909091           0.818182   \n",
       "10         entropy               7           0.909091           0.909091   \n",
       "0             gini               3           0.863636           0.909091   \n",
       "6          entropy               3           0.863636           0.818182   \n",
       "7          entropy               4           0.863636           0.818182   \n",
       "1             gini               4           0.863636           0.818182   \n",
       "8          entropy               5           0.863636           0.772727   \n",
       "\n",
       "    split2_test_score  split3_test_score  split4_test_score  mean_test_score  \\\n",
       "11           0.863636           0.636364           0.666667         0.787879   \n",
       "5            0.772727           0.681818           0.619048         0.769264   \n",
       "3            0.818182           0.681818           0.523810         0.759307   \n",
       "2            0.818182           0.590909           0.619048         0.751082   \n",
       "4            0.772727           0.590909           0.619048         0.751082   \n",
       "9            0.818182           0.636364           0.571429         0.750649   \n",
       "10           0.818182           0.590909           0.523810         0.750216   \n",
       "0            0.772727           0.636364           0.428571         0.722078   \n",
       "6            0.772727           0.636364           0.476190         0.713420   \n",
       "7            0.818182           0.545455           0.476190         0.704329   \n",
       "1            0.772727           0.545455           0.476190         0.695238   \n",
       "8            0.727273           0.590909           0.476190         0.686147   \n",
       "\n",
       "    std_test_score  rank_test_score  \n",
       "11        0.112978                1  \n",
       "5         0.108334                2  \n",
       "3         0.140178                3  \n",
       "2         0.120771                4  \n",
       "4         0.127430                4  \n",
       "9         0.126021                6  \n",
       "10        0.162321                7  \n",
       "0         0.173822                8  \n",
       "6         0.140906                9  \n",
       "7         0.160370               10  \n",
       "1         0.154851               11  \n",
       "8         0.136998               12  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['param_criterion', 'param_max_depth', 'split0_test_score',\n",
      "       'split1_test_score', 'split2_test_score', 'split3_test_score',\n",
      "       'split4_test_score', 'mean_test_score', 'std_test_score',\n",
      "       'rank_test_score'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "cv_result = pd.DataFrame(grid_result.cv_results_)\n",
    "cv_result = cv_result[['param_criterion', 'param_max_depth',\n",
    "                       'split0_test_score',\n",
    "                       'split1_test_score', 'split2_test_score', 'split3_test_score',\n",
    "                       'split4_test_score', 'mean_test_score', 'std_test_score',\n",
    "                       'rank_test_score']]\n",
    "cv_result = cv_result.sort_values(by='rank_test_score')\n",
    "display(cv_result)\n",
    "print(cv_result.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model performance evaluation with the best found model in grid search and cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(grid_result, \n",
    "                      X_test, \n",
    "                      y_test, \n",
    "                      display_labels=[\"Doesn't have HD\", \"Has HD\"],\n",
    "                      cmap='Greys')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion_matrix: \n",
      " [[25  7]\n",
      " [ 6 22]]\n",
      "Accuracy:  0.7833333333333333\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.78      0.79        32\n",
      "           1       0.76      0.79      0.77        28\n",
      "\n",
      "    accuracy                           0.78        60\n",
      "   macro avg       0.78      0.78      0.78        60\n",
      "weighted avg       0.78      0.78      0.78        60\n",
      "\n"
     ]
    }
   ],
   "source": [
    "confusion_matrix_dt = confusion_matrix(y_test, y_pred)\n",
    "print('confusion_matrix: \\n', confusion_matrix_dt)\n",
    "\n",
    "print('Accuracy: ', accuracy_score(y_test, y_pred))\n",
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, thr = roc_curve(y_test, grid_result.predict_proba(X_test)[:,1])\n",
    "auc = np.round(roc_auc_score(y_test, y_pred), 2)\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.plot(fpr, \n",
    "         tpr, \n",
    "         color='green', \n",
    "         lw=2, \n",
    "         label=\"Curve Area = \" +str(auc))\n",
    "\n",
    "plt.plot([0, 1], [0, 1], color='blue', lw=2, linestyle='--')\n",
    "plt.xlabel('False Positive Rate (FPR)')\n",
    "plt.ylabel('True Positive Rate (TPR)')\n",
    "plt.title('ROC curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}