ml_models/adult_k5_l2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6bd3fd6d",
   "metadata": {},
   "source": [
    "## Machine Learning models. Case $k=5$ and $\\ell=2$.\n",
    "* *k-Nearest Neigbors*\n",
    "* *Random Forest*\n",
    "* *Adaptive Boosting*\n",
    "* *Gradient Tree Boosting*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ed3b62f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "49aa0e34",
   "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>education</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>sex</th>\n",
       "      <th>native-country</th>\n",
       "      <th>salary-class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[40, 80[</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Nontechnical</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>?</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[40, 80[</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Nontechnical</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>?</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[40, 80[</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Nontechnical</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>?</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[40, 80[</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Nontechnical</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>?</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[40, 80[</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Nontechnical</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>?</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age education    occupation relationship   sex native-country  \\\n",
       "0  [40, 80[  Graduate  Nontechnical      Husband  Male              ?   \n",
       "1  [40, 80[  Graduate  Nontechnical      Husband  Male              ?   \n",
       "2  [40, 80[  Graduate  Nontechnical      Husband  Male              ?   \n",
       "3  [40, 80[  Graduate  Nontechnical      Husband  Male              ?   \n",
       "4  [40, 80[  Graduate  Nontechnical      Husband  Male              ?   \n",
       "\n",
       "  salary-class  \n",
       "0         >50K  \n",
       "1         >50K  \n",
       "2         >50K  \n",
       "3         >50K  \n",
       "4        <=50K  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../data/adult_k5_l2_new.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f299dd3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "age = preprocessing.LabelEncoder().fit(df['age'])\n",
    "df['age'] = age.transform(df['age'])\n",
    "\n",
    "education = preprocessing.LabelEncoder().fit(df['education'])\n",
    "df['education'] = education.transform(df['education'])\n",
    "\n",
    "relationship = preprocessing.LabelEncoder().fit(df['relationship'])\n",
    "df['relationship'] = relationship.transform(df['relationship'])\n",
    "\n",
    "occupation = preprocessing.LabelEncoder().fit(df['occupation'])\n",
    "df['occupation'] = occupation.transform(df['occupation'])\n",
    "\n",
    "sex = preprocessing.LabelEncoder().fit(df['sex'])\n",
    "df['sex'] = sex.transform(df['sex'])\n",
    "\n",
    "nc = preprocessing.LabelEncoder().fit(df['native-country'])\n",
    "df['native-country'] = nc.transform(df['native-country'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "313a12e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['age', 'education', 'relationship', 'occupation', 'sex', 'native-country']].values\n",
    "y = df['salary-class'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "91a421ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42, stratify = y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d66f4b32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42),\n",
       "             param_grid={&#x27;max_depth&#x27;: [2, 3, 4, 5, 6, 7, 8, 9]})</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42),\n",
       "             param_grid={&#x27;max_depth&#x27;: [2, 3, 4, 5, 6, 7, 8, 9]})</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 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 sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator 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 sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42),\n",
       "             param_grid={'max_depth': [2, 3, 4, 5, 6, 7, 8, 9]})"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators=100, criterion='gini', random_state=42)\n",
    "param_grid = {'max_depth': [2, 3, 4, 5, 6, 7, 8, 9]}\n",
    "grid_clf = GridSearchCV(rf, param_grid, cv=5, refit=True)\n",
    "grid_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b6509716",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8024620669911251"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = grid_clf.best_estimator_\n",
    "rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "99d07eec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
       "             param_grid={&#x27;n_neighbors&#x27;: [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n",
       "                                         14, 15, 16, 17, 18, 19, 20, 21, 22, 23,\n",
       "                                         24, 25, 26, 27, 28, 29, 30, 31, 32, ...]})</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 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 sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
       "             param_grid={&#x27;n_neighbors&#x27;: [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n",
       "                                         14, 15, 16, 17, 18, 19, 20, 21, 22, 23,\n",
       "                                         24, 25, 26, 27, 28, 29, 30, 31, 32, ...]})</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 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 sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator 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 sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
       "             param_grid={'n_neighbors': [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n",
       "                                         14, 15, 16, 17, 18, 19, 20, 21, 22, 23,\n",
       "                                         24, 25, 26, 27, 28, 29, 30, 31, 32, ...]})"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(metric='minkowski')\n",
    "param_grid = {'n_neighbors': list(range(3,51))}\n",
    "grid_knn = GridSearchCV(knn, param_grid, cv=5, refit=True)\n",
    "grid_knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8b5e709e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7894360148869167"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = grid_knn.best_estimator_\n",
    "knn.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ac507404",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=GradientBoostingClassifier(random_state=42),\n",
       "             param_grid={&#x27;learning_rate&#x27;: [0.01, 0.1, 0.5, 1],\n",
       "                         &#x27;max_depth&#x27;: [2, 4, 6, 8, 10],\n",
       "                         &#x27;n_estimators&#x27;: [50, 100, 150]})</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=GradientBoostingClassifier(random_state=42),\n",
       "             param_grid={&#x27;learning_rate&#x27;: [0.01, 0.1, 0.5, 1],\n",
       "                         &#x27;max_depth&#x27;: [2, 4, 6, 8, 10],\n",
       "                         &#x27;n_estimators&#x27;: [50, 100, 150]})</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=GradientBoostingClassifier(random_state=42),\n",
       "             param_grid={'learning_rate': [0.01, 0.1, 0.5, 1],\n",
       "                         'max_depth': [2, 4, 6, 8, 10],\n",
       "                         'n_estimators': [50, 100, 150]})"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb = GradientBoostingClassifier(random_state=42)\n",
    "param_grid = {'n_estimators': [50, 100, 150],\n",
    "                  'learning_rate': [0.01, 0.1, 0.5, 1],\n",
    "                  'max_depth': [2, 4, 6, 8, 10]}\n",
    "grid_gb = GridSearchCV(gb, param_grid, cv=5, refit=True)\n",
    "grid_gb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c3cdc467",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8024620669911251"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb = grid_gb.best_estimator_\n",
    "gb.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "64eef381",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=AdaBoostClassifier(random_state=42),\n",
       "             param_grid={&#x27;learning_rate&#x27;: [0.01, 0.1, 0.5, 1],\n",
       "                         &#x27;n_estimators&#x27;: [50, 100, 150]})</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=AdaBoostClassifier(random_state=42),\n",
       "             param_grid={&#x27;learning_rate&#x27;: [0.01, 0.1, 0.5, 1],\n",
       "                         &#x27;n_estimators&#x27;: [50, 100, 150]})</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=AdaBoostClassifier(random_state=42),\n",
       "             param_grid={'learning_rate': [0.01, 0.1, 0.5, 1],\n",
       "                         'n_estimators': [50, 100, 150]})"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ab = AdaBoostClassifier(random_state=42)\n",
    "param_grid = {'n_estimators': [50, 100, 150], 'learning_rate': [0.01, 0.1, 0.5, 1]}\n",
    "grid_ab = GridSearchCV(ab, param_grid, cv=5, refit=True)\n",
    "grid_ab.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "23cc2864",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8017463498425422"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ab = grid_ab.best_estimator_\n",
    "ab.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0a9971dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, roc_curve, auc, f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a15b8861",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       <=50K       0.84      0.91      0.87      5137\n",
      "        >50K       0.67      0.50      0.57      1849\n",
      "\n",
      "    accuracy                           0.80      6986\n",
      "   macro avg       0.75      0.71      0.72      6986\n",
      "weighted avg       0.79      0.80      0.79      6986\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# RF:\n",
    "y_pred = rf.predict(X_test)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7ca66958",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7066167434153834\n"
     ]
    }
   ],
   "source": [
    "y_test_bin = [0 if y == '<=50K' else 1 for y in y_test]\n",
    "y_pred_bin = [0 if y == '<=50K' else 1 for y in y_pred]\n",
    "fpr_rf, tpr_rf, thresholds = roc_curve(y_test_bin, y_pred_bin)\n",
    "print(auc(fpr_rf, tpr_rf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fb16b42a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5740740740740742\n"
     ]
    }
   ],
   "source": [
    "print(f1_score(y_test_bin, y_pred_bin))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8adc9bb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       <=50K       0.83      0.91      0.87      5137\n",
      "        >50K       0.67      0.49      0.57      1849\n",
      "\n",
      "    accuracy                           0.80      6986\n",
      "   macro avg       0.75      0.70      0.72      6986\n",
      "weighted avg       0.79      0.80      0.79      6986\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# GB:\n",
    "y_pred = gb.predict(X_test)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "93ceedb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.704020492902266\n"
     ]
    }
   ],
   "source": [
    "y_test_bin = [0 if y == '<=50K' else 1 for y in y_test]\n",
    "y_pred_bin = [0 if y == '<=50K' else 1 for y in y_pred]\n",
    "fpr_gb, tpr_gb, thresholds = roc_curve(y_test_bin, y_pred_bin)\n",
    "print(auc(fpr_gb, tpr_gb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1ff532b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5700934579439253\n"
     ]
    }
   ],
   "source": [
    "print(f1_score(y_test_bin, y_pred_bin))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "83227f41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       <=50K       0.83      0.91      0.87      5137\n",
      "        >50K       0.67      0.50      0.57      1849\n",
      "\n",
      "    accuracy                           0.80      6986\n",
      "   macro avg       0.75      0.70      0.72      6986\n",
      "weighted avg       0.79      0.80      0.79      6986\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# AB:\n",
    "y_pred = ab.predict(X_test)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8fb586c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7043992443710794\n"
     ]
    }
   ],
   "source": [
    "y_test_bin = [0 if y == '<=50K' else 1 for y in y_test]\n",
    "y_pred_bin = [0 if y == '<=50K' else 1 for y in y_pred]\n",
    "fpr_ab, tpr_ab, thresholds = roc_curve(y_test_bin, y_pred_bin)\n",
    "print(auc(fpr_ab, tpr_ab))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "03bea0cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5705426356589147\n"
     ]
    }
   ],
   "source": [
    "print(f1_score(y_test_bin, y_pred_bin))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "aeca9af3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       <=50K       0.85      0.87      0.86      5137\n",
      "        >50K       0.61      0.56      0.58      1849\n",
      "\n",
      "    accuracy                           0.79      6986\n",
      "   macro avg       0.73      0.71      0.72      6986\n",
      "weighted avg       0.78      0.79      0.79      6986\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# kNN:\n",
    "y_pred = knn.predict(X_test)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d1ec1b33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7145485203530353\n"
     ]
    }
   ],
   "source": [
    "y_test_bin = [0 if y == '<=50K' else 1 for y in y_test]\n",
    "y_pred_bin = [0 if y == '<=50K' else 1 for y in y_pred]\n",
    "fpr_knn, tpr_knn, thresholds = roc_curve(y_test_bin, y_pred_bin)\n",
    "print(auc(fpr_knn, tpr_knn))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e6a9b1aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5826950354609929\n"
     ]
    }
   ],
   "source": [
    "print(f1_score(y_test_bin, y_pred_bin))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2036129d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots(1, 1, figsize = (7, 6))\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.plot(fpr_knn, tpr_knn, label='kNN (AUC = {:.4f})'.format(auc(fpr_knn, tpr_knn)), color = '#2a40cb')\n",
    "plt.plot(fpr_rf, tpr_rf, label='RF (AUC = {:.4f})'.format(auc(fpr_rf, tpr_rf)), color = '#e23d15')\n",
    "plt.plot(fpr_gb, tpr_gb, label='GB (AUC = {:.4f})'.format(auc(fpr_gb, tpr_gb)), color = '#11bf2e')\n",
    "plt.plot(fpr_ab, tpr_ab, label='AB (AUC = {:.4f})'.format(auc(fpr_ab, tpr_ab)), color = '#c2cd0f')\n",
    "plt.xlabel('FPR (false positive rate)')\n",
    "plt.ylabel('TPR (true positive rate)')\n",
    "plt.title('ROC curves for each Machine Learning model. Case: k=5, $\\ell$=2')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b810655d",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig('roc_k5_l2.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f9257c41",
   "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>kNN</th>\n",
       "      <th>RF</th>\n",
       "      <th>GB</th>\n",
       "      <th>AB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.126338</td>\n",
       "      <td>0.089741</td>\n",
       "      <td>0.086821</td>\n",
       "      <td>0.088768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.555435</td>\n",
       "      <td>0.502975</td>\n",
       "      <td>0.494862</td>\n",
       "      <td>0.497566</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        kNN        RF        GB        AB\n",
       "0  0.126338  0.089741  0.086821  0.088768\n",
       "1  0.555435  0.502975  0.494862  0.497566"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_k5_l2 = pd.DataFrame({'kNN': [fpr_knn[1], tpr_knn[1]],\n",
    "           'RF': [fpr_rf[1], tpr_rf[1]],\n",
    "           'GB': [fpr_gb[1], tpr_gb[1]],\n",
    "           'AB': [fpr_ab[1], tpr_ab[1]]})\n",
    "roc_k5_l2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f6597bf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "roc_k5_l2.to_csv('roc_k5_l2.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6637c4c2",
   "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}