decision_trees_cross-validation_gridsearch.ipynb

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   "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)"
   ]
  },
  {
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   "metadata": {
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       "<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",
       "      <th>num</th>\n",
       "    </tr>\n",
       "  </thead>\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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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",
       "\n",
       "   slope   ca thal  num  \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  "
      ]
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   ],
   "source": [
    "col_names = ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', \n",
    "             'thalach', 'exang', 'oldpeak', 'slope', 'ca', 'thal', 'num']\n",
    "\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()"
   ]
  },
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   "execution_count": 3,
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    {
     "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        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": 4,
   "metadata": {
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    "outputId": "ade3792d-0566-4ac4-a01d-a4810fc7328e"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
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   "metadata": {
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    "outputId": "e16e9238-22b7-498f-ac65-ff292953c963"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 1, 3, 4], dtype=int64)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.num.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "executionInfo": {
     "elapsed": 1,
     "status": "ok",
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     },
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    },
    "id": "il7JO1twHnfk"
   },
   "outputs": [],
   "source": [
    "df['num'] = np.where(df.num == 0, 0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
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     "execution_count": 7,
     "metadata": {},
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   "source": [
    "df.num.unique()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
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    },
    "id": "_I5mS6mQHnfl",
    "outputId": "e5f3ce42-4f5b-456b-b650-6d29666c52e3"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0.0', '3.0', '2.0', '1.0', '?'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ca'].unique()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 188,
     "status": "ok",
     "timestamp": 1643682665942,
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      "displayName": "Ilyas Ustun",
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      "userId": "07102234121230289250"
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    },
    "id": "xeKdBckrHnfm",
    "outputId": "5a7cca63-b021-4074-81c3-481a33b97d18"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['6.0', '3.0', '7.0', '?'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.thal.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "executionInfo": {
     "elapsed": 129,
     "status": "ok",
     "timestamp": 1643682666697,
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      "displayName": "Ilyas Ustun",
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      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "6iVe0RnOHnfm"
   },
   "outputs": [],
   "source": [
    "df.replace('?', np.nan, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
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     "status": "ok",
     "timestamp": 1643682667632,
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    "id": "XW6iL5KuHnfm",
    "outputId": "aebb1af3-5727-4d08-d396-9517c8d3b3a1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0.0', '3.0', '2.0', '1.0', nan], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
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   ],
   "source": [
    "df['ca'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "executionInfo": {
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     "status": "ok",
     "timestamp": 1643682668335,
     "user": {
      "displayName": "Ilyas Ustun",
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      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "ls3IiEJ-Hnfn"
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   "outputs": [],
   "source": [
    "df.ca = df.ca.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "executionInfo": {
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     "timestamp": 1643682668973,
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     },
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    },
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   "outputs": [],
   "source": [
    "df.thal = df.thal.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
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    "id": "-RxEkOeQHnfn",
    "outputId": "21b940e2-4f89-4a68-de9e-5b97acc8b815"
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    {
     "data": {
      "text/plain": [
       "array([ 0.,  3.,  2.,  1., nan])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ca'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
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     "status": "ok",
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    {
     "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()"
   ]
  },
  {
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   "execution_count": 16,
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   "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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "executionInfo": {
     "elapsed": 128,
     "status": "ok",
     "timestamp": 1643682680355,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "Z1km9NluHnfp"
   },
   "outputs": [],
   "source": [
    "X = df.drop('num', axis = 1)\n",
    "y = df.num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "executionInfo": {
     "elapsed": 381,
     "status": "ok",
     "timestamp": 1643682681901,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "g3JvhbO2Hnfp"
   },
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "executionInfo": {
     "elapsed": 125,
     "status": "ok",
     "timestamp": 1643682683390,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "iqXZSSplHnfq"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Pl03c3QDHnfq"
   },
   "source": [
    "## Imputing NaN\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "executionInfo": {
     "elapsed": 370,
     "status": "ok",
     "timestamp": 1643682687224,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "RQRTLe23Hnfq"
   },
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "executionInfo": {
     "elapsed": 120,
     "status": "ok",
     "timestamp": 1643682687644,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "h0p_RDqUHnfq"
   },
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer = SimpleImputer(strategy = 'most_frequent')\n",
    "imputer.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "executionInfo": {
     "elapsed": 103,
     "status": "ok",
     "timestamp": 1643682688826,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "fy5ToTRKHnfr"
   },
   "outputs": [],
   "source": [
    "X_train = imputer.transform(X_train)\n",
    "X_test = imputer.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 117,
     "status": "ok",
     "timestamp": 1643682689469,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "-rY3S6LlHnfr",
    "outputId": "6ca77deb-8ea6-476a-91f4-bd404bd1bdaf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.isnan(X_train).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 91,
     "status": "ok",
     "timestamp": 1643682691111,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "ALcJbK_hHnfr",
    "outputId": "ad473dbd-9b80-4e8b-d437-cf50910b5494"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.isnan(X_test).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 116,
     "status": "ok",
     "timestamp": 1643682706970,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "mK1nOxeLH63r",
    "outputId": "d5f52945-9594-4b76-83bc-b447a23b0162"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[45.,  0.,  4., ...,  2.,  0.,  3.],\n",
       "       [63.,  1.,  1., ...,  3.,  0.,  6.],\n",
       "       [42.,  1.,  4., ...,  2.,  0.,  6.],\n",
       "       ...,\n",
       "       [64.,  0.,  4., ...,  2.,  2.,  3.],\n",
       "       [53.,  1.,  4., ...,  2.,  2.,  7.],\n",
       "       [66.,  1.,  4., ...,  1.,  0.,  6.]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0qg6IUfOHnfr"
   },
   "source": [
    "## Decision Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "executionInfo": {
     "elapsed": 96,
     "status": "ok",
     "timestamp": 1643682737170,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "xLhf9DA_Hnfs"
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "executionInfo": {
     "elapsed": 267,
     "status": "ok",
     "timestamp": 1643682812751,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "ZcYl1TGuHnfs"
   },
   "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>DecisionTreeClassifier()</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "executionInfo": {
     "elapsed": 113,
     "status": "ok",
     "timestamp": 1643682818714,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "UJ85paSbHnft"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1,\n",
       "       0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = dt.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 107,
     "status": "ok",
     "timestamp": 1643682832981,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "RLLKJE9iHnft",
    "outputId": "cbbeba04-368b-4ea8-98e0-730f518ba374"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7868852459016393"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 97,
     "status": "ok",
     "timestamp": 1643682859543,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "qHu8ozVvHnft",
    "outputId": "ff932c09-9878-4c94-f9c8-6f7606e3cb6a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.82      0.81        33\n",
      "           1       0.78      0.75      0.76        28\n",
      "\n",
      "    accuracy                           0.79        61\n",
      "   macro avg       0.79      0.78      0.78        61\n",
      "weighted avg       0.79      0.79      0.79        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TJLrZwV4Hnft"
   },
   "source": [
    "### Apply cross validation using default tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "executionInfo": {
     "elapsed": 195,
     "status": "ok",
     "timestamp": 1643682901995,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "Ym8K59EWHnfu"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import StratifiedKFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "executionInfo": {
     "elapsed": 196,
     "status": "ok",
     "timestamp": 1643682920206,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "-78_EV4AHnfu"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StratifiedKFold(n_splits=10, random_state=0, shuffle=True)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv = StratifiedKFold(n_splits = 10, shuffle = True, random_state = 0)\n",
    "cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1409,
     "status": "ok",
     "timestamp": 1643682993449,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "hqkoe0RkHnfu",
    "outputId": "64fd19f4-90a9-4273-8a79-54e9d49ebecd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV accuracy scores: [0.92       0.76       0.66666667 0.70833333 0.95833333 0.66666667\n",
      " 0.70833333 0.875      0.83333333 0.83333333]\n"
     ]
    }
   ],
   "source": [
    "dt = DecisionTreeClassifier()\n",
    "scores = cross_val_score(dt, X = X_train, y = y_train, scoring = 'accuracy', cv = cv, n_jobs = -1)\n",
    "print('CV accuracy scores: %s' % scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 126,
     "status": "ok",
     "timestamp": 1643683030723,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "xxSw-lVjHnfu",
    "outputId": "cdf3ff99-9403-45ed-ff53-5be824fcfcff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV accuracy: 0.793 +/- 0.100\n"
     ]
    }
   ],
   "source": [
    "print('CV accuracy: %.3f +/- %.3f' % (np.mean(scores), np.std(scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zaTAE2RbHnfu"
   },
   "source": [
    "### Apply cross-validation to find an optimum parameter value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "tARgaGQDHnfv",
    "outputId": "71d58fc5-cd80-49ad-dcbc-b610dfeb7c4e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(242, 13)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "executionInfo": {
     "elapsed": 127,
     "status": "ok",
     "timestamp": 1643683132149,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "lF93QDl1Hnfv"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.7849999999999999,\n",
       " 0.8098333333333333,\n",
       " 0.8303333333333335,\n",
       " 0.8261666666666667,\n",
       " 0.8220000000000001,\n",
       " 0.7274999999999999,\n",
       " 0.7191666666666666]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_samples_split = [5, 10, 20, 25, 30, 40, 50]\n",
    "scores_params = []\n",
    "\n",
    "for m in min_samples_split:\n",
    "    dt = DecisionTreeClassifier(min_samples_split = m)\n",
    "    scores = cross_val_score(dt, X = X_train, y = y_train, scoring = 'accuracy', cv = cv)\n",
    "    scores_params.append(scores.mean())\n",
    "\n",
    "scores_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 302
    },
    "executionInfo": {
     "elapsed": 350,
     "status": "ok",
     "timestamp": 1643683183730,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "rbHIM8HxHnfv",
    "outputId": "7b5638db-4a20-4983-af1f-0f04dabb4755"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(scores_params, label = 'cv')\n",
    "plt.xticks(np.arange(len(min_samples_split)), min_samples_split, rotation = 45)\n",
    "plt.xlabel('min_samples_split')\n",
    "plt.ylabel('Score')\n",
    "plt.title('Validation Curve')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "r8qfrjA7Hnfw"
   },
   "source": [
    "### Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "executionInfo": {
     "elapsed": 109,
     "status": "ok",
     "timestamp": 1643683344079,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "GIgDs090Hnfw"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 109,
     "status": "ok",
     "timestamp": 1643683420030,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "xQOxfRbwHnfw",
    "outputId": "cdb8cfc0-d15b-47e9-9b73-e9efcfcfb38d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'min_samples_split': [5, 10, 20, 30, 40, 50], 'max_depth': [1, 2, 3, None]}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = dict(\n",
    "    min_samples_split = [5, 10, 20, 30, 40, 50],\n",
    "    max_depth = [1, 2, 3, None],\n",
    ")\n",
    "params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "rzQscw2aHnfw",
    "outputId": "bc05eef5-04ba-4652-ddba-bf2571dd8bf1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 137,
     "status": "ok",
     "timestamp": 1643683469752,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "Ab0Fvsk-Hnfw",
    "outputId": "99289026-fd14-43ab-e24c-f57e51355669"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\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-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3 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-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 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-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3 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-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 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-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=0, shuffle=True),\n",
       "             estimator=DecisionTreeClassifier(), n_jobs=-1,\n",
       "             param_grid={&#x27;max_depth&#x27;: [1, 2, 3, None],\n",
       "                         &#x27;min_samples_split&#x27;: [5, 10, 20, 30, 40, 50]},\n",
       "             scoring=&#x27;f1&#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  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 \">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=0, shuffle=True),\n",
       "             estimator=DecisionTreeClassifier(), n_jobs=-1,\n",
       "             param_grid={&#x27;max_depth&#x27;: [1, 2, 3, None],\n",
       "                         &#x27;min_samples_split&#x27;: [5, 10, 20, 30, 40, 50]},\n",
       "             scoring=&#x27;f1&#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  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 \">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content \"><pre>DecisionTreeClassifier()</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-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a></label><div class=\"sk-toggleable__content \"><pre>DecisionTreeClassifier()</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=0, shuffle=True),\n",
       "             estimator=DecisionTreeClassifier(), n_jobs=-1,\n",
       "             param_grid={'max_depth': [1, 2, 3, None],\n",
       "                         'min_samples_split': [5, 10, 20, 30, 40, 50]},\n",
       "             scoring='f1')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = DecisionTreeClassifier()\n",
    "gs = GridSearchCV(estimator = dt, param_grid = params, scoring = 'f1', cv = cv, n_jobs = -1, )\n",
    "gs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1817,
     "status": "ok",
     "timestamp": 1643683491653,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "Lubj2j87Hnfx",
    "outputId": "bf30ee22-dc13-4455-db7a-c2a1c90f8626"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\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-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 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-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 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-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 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-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 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-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=0, shuffle=True),\n",
       "             estimator=DecisionTreeClassifier(), n_jobs=-1,\n",
       "             param_grid={&#x27;max_depth&#x27;: [1, 2, 3, None],\n",
       "                         &#x27;min_samples_split&#x27;: [5, 10, 20, 30, 40, 50]},\n",
       "             scoring=&#x27;f1&#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-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=0, shuffle=True),\n",
       "             estimator=DecisionTreeClassifier(), n_jobs=-1,\n",
       "             param_grid={&#x27;max_depth&#x27;: [1, 2, 3, None],\n",
       "                         &#x27;min_samples_split&#x27;: [5, 10, 20, 30, 40, 50]},\n",
       "             scoring=&#x27;f1&#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-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</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-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=0, shuffle=True),\n",
       "             estimator=DecisionTreeClassifier(), n_jobs=-1,\n",
       "             param_grid={'max_depth': [1, 2, 3, None],\n",
       "                         'min_samples_split': [5, 10, 20, 30, 40, 50]},\n",
       "             scoring='f1')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 109,
     "status": "ok",
     "timestamp": 1643683510151,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "x6jiX4BWK8nW",
    "outputId": "b5639d86-15f5-467b-8db8-4eaa2e125211"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\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-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 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-5 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-5 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-5 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-5 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-5 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-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 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-5 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-5 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-5 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-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 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-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 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-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 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-5 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-5 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-5 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-5 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-5 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-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 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-5 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-5 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-5 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-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 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-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 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-5 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-5 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-5 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-5 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-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=3, min_samples_split=5)</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-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=3, min_samples_split=5)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=3, min_samples_split=5)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 99,
     "status": "ok",
     "timestamp": 1643683595605,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "FU8BQElPHnfx",
    "outputId": "9701c539-f205-4312-876e-32d5adf19518"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ccp_alpha': 0.0,\n",
       " 'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': 3,\n",
       " 'max_features': None,\n",
       " 'max_leaf_nodes': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 5,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'monotonic_cst': None,\n",
       " 'random_state': None,\n",
       " 'splitter': 'best'}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.best_estimator_.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 110,
     "status": "ok",
     "timestamp": 1643683623381,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "n0H1iaBlHnfx",
    "outputId": "1566aded-dad9-4fca-f51f-793a7ae97a52"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 3, 'min_samples_split': 5}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "executionInfo": {
     "elapsed": 117,
     "status": "ok",
     "timestamp": 1643683634121,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "imeKN75qHnfx"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_estimator = gs.best_estimator_\n",
    "best_estimator.get_depth()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "executionInfo": {
     "elapsed": 227,
     "status": "ok",
     "timestamp": 1643683688095,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "T6xxpyCxHnfx"
   },
   "outputs": [],
   "source": [
    "pred = best_estimator.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 112,
     "status": "ok",
     "timestamp": 1643683689101,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "bDJu24VEHnfx",
    "outputId": "39b55ce8-ba92-4a8a-dbd9-2b45604e54da"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8360655737704918"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(y_test, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 103,
     "status": "ok",
     "timestamp": 1643683695007,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "41OwW-g7Hnfx",
    "outputId": "284f17a0-06c9-41e3-908e-000fda047d12"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.94      0.86        33\n",
      "           1       0.91      0.71      0.80        28\n",
      "\n",
      "    accuracy                           0.84        61\n",
      "   macro avg       0.85      0.83      0.83        61\n",
      "weighted avg       0.85      0.84      0.83        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 213,
     "status": "ok",
     "timestamp": 1643683706276,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "QgC-i6F3Hnfx",
    "outputId": "4310ea95-f24e-4dfe-dc1f-ed61029d5338"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[31,  2],\n",
       "       [ 8, 20]], dtype=int64)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.confusion_matrix(y_test, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "executionInfo": {
     "elapsed": 128,
     "status": "ok",
     "timestamp": 1643683708302,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "u1F9ymA3Hnfy",
    "outputId": "39781775-bacc-42ff-b1c2-abba59c5382d"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metrics.ConfusionMatrixDisplay.from_predictions(y_test, pred, colorbar = False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "executionInfo": {
     "elapsed": 243,
     "status": "ok",
     "timestamp": 1643683762556,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "SJMSDsW6Hnfy",
    "outputId": "a25cb614-615a-406c-e627-8d76071f120b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_feature_importances(model, df):\n",
    "    n_features = df.shape[1]\n",
    "    df_colnames = df.columns\n",
    "    plt.barh(np.arange(n_features), model.feature_importances_, align = 'center')\n",
    "    plt.yticks(np.arange(n_features), df_colnames)\n",
    "    plt.xlabel(\"Feature importance\")\n",
    "    plt.ylim(-1, n_features)\n",
    "\n",
    "plot_feature_importances(best_estimator, df = X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EBNQmB-6Hnfy"
   },
   "source": [
    "### Learning curve using sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "executionInfo": {
     "elapsed": 121,
     "status": "ok",
     "timestamp": 1643683820327,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "558GXy27Hnfy"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import learning_curve, validation_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "executionInfo": {
     "elapsed": 118,
     "status": "ok",
     "timestamp": 1643683830001,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "amZP3U6EHnfy"
   },
   "outputs": [],
   "source": [
    "dt = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "executionInfo": {
     "elapsed": 518,
     "status": "ok",
     "timestamp": 1643683860399,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "EXnuPvvvHnfy"
   },
   "outputs": [],
   "source": [
    "train_sizes, train_scores, test_scores = learning_curve(estimator = best_estimator,\n",
    "                                            X = X_train,\n",
    "                                            y = y_train,\n",
    "                                            train_sizes = np.linspace(0.02, 1.0, 20),\n",
    "                                            cv = cv,\n",
    "                                            n_jobs = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 118,
     "status": "ok",
     "timestamp": 1643683862711,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "_agkQL23Hnfy",
    "outputId": "5b7cd6dc-075d-4f6d-f9a7-12499538a954"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.02      , 0.07157895, 0.12315789, 0.17473684, 0.22631579,\n",
       "       0.27789474, 0.32947368, 0.38105263, 0.43263158, 0.48421053,\n",
       "       0.53578947, 0.58736842, 0.63894737, 0.69052632, 0.74210526,\n",
       "       0.79368421, 0.84526316, 0.89684211, 0.94842105, 1.        ])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0.02, 1.0, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "id": "KCH0E_vTHnfy",
    "outputId": "11d1e957-0106-48d8-ef90-8f4a43fa911d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "242"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 115,
     "status": "ok",
     "timestamp": 1643683899718,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "ICliEilgHnfz",
    "outputId": "b3c3c3ec-eeba-413b-fa4d-cafe2b61c8bb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.356"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape[0] * 0.02 * 0.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 117,
     "status": "ok",
     "timestamp": 1643683907453,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "qf70iaB1Hnfz",
    "outputId": "643dc475-6642-425e-ea60-f0f9ae18ad5f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "217.8"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape[0] * 1 * 0.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 112,
     "status": "ok",
     "timestamp": 1643683919926,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "fjbNnBYwHnfz",
    "outputId": "86fe340e-ffa8-42b5-8ce9-3f5ae5d9f61e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  4,  15,  26,  37,  49,  60,  71,  82,  93, 105, 116, 127, 138,\n",
       "       149, 161, 172, 183, 194, 205, 217])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 117,
     "status": "ok",
     "timestamp": 1643683924831,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "1ysjdxhWHnfz",
    "outputId": "2bfece20-b716-4c7b-dd01-8ab38793cd20"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.75      , 0.5       , 0.5       , 0.5       , 0.75      ,\n",
       "        0.5       , 0.5       , 0.5       , 0.5       , 0.5       ],\n",
       "       [1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "        1.        , 0.93333333, 1.        , 1.        , 1.        ],\n",
       "       [0.96153846, 0.96153846, 0.96153846, 0.92307692, 0.92307692,\n",
       "        0.92307692, 0.96153846, 0.92307692, 0.92307692, 0.92307692],\n",
       "       [0.94594595, 0.94594595, 0.91891892, 0.94594595, 0.91891892,\n",
       "        0.91891892, 0.91891892, 0.94594595, 0.91891892, 0.91891892],\n",
       "       [0.91836735, 0.95918367, 0.95918367, 0.93877551, 0.95918367,\n",
       "        0.93877551, 0.91836735, 0.95918367, 0.93877551, 0.95918367],\n",
       "       [0.86666667, 0.95      , 0.95      , 0.91666667, 0.95      ,\n",
       "        0.9       , 0.91666667, 0.91666667, 0.91666667, 0.93333333],\n",
       "       [0.87323944, 0.90140845, 0.85915493, 0.88732394, 0.91549296,\n",
       "        0.90140845, 0.91549296, 0.90140845, 0.87323944, 0.90140845],\n",
       "       [0.90243902, 0.8902439 , 0.8902439 , 0.90243902, 0.92682927,\n",
       "        0.90243902, 0.8902439 , 0.91463415, 0.90243902, 0.90243902],\n",
       "       [0.89247312, 0.86021505, 0.89247312, 0.90322581, 0.90322581,\n",
       "        0.90322581, 0.89247312, 0.90322581, 0.89247312, 0.89247312],\n",
       "       [0.88571429, 0.85714286, 0.9047619 , 0.8952381 , 0.9047619 ,\n",
       "        0.8952381 , 0.9047619 , 0.91428571, 0.8952381 , 0.8952381 ],\n",
       "       [0.87931034, 0.88793103, 0.89655172, 0.88793103, 0.89655172,\n",
       "        0.90517241, 0.89655172, 0.9137931 , 0.90517241, 0.89655172],\n",
       "       [0.88976378, 0.8976378 , 0.8976378 , 0.88976378, 0.8976378 ,\n",
       "        0.90551181, 0.88976378, 0.90551181, 0.8976378 , 0.8976378 ],\n",
       "       [0.86231884, 0.87681159, 0.89855072, 0.88405797, 0.88405797,\n",
       "        0.88405797, 0.88405797, 0.89130435, 0.88405797, 0.89855072],\n",
       "       [0.8590604 , 0.87919463, 0.86577181, 0.86577181, 0.87248322,\n",
       "        0.88590604, 0.87919463, 0.87248322, 0.87248322, 0.88590604],\n",
       "       [0.85093168, 0.86956522, 0.86335404, 0.88198758, 0.86335404,\n",
       "        0.86956522, 0.86956522, 0.86956522, 0.85714286, 0.90062112],\n",
       "       [0.85465116, 0.88953488, 0.86627907, 0.87790698, 0.88953488,\n",
       "        0.89534884, 0.85465116, 0.88372093, 0.87790698, 0.87209302],\n",
       "       [0.8852459 , 0.8852459 , 0.8579235 , 0.87431694, 0.87978142,\n",
       "        0.89071038, 0.86338798, 0.8852459 , 0.86885246, 0.87431694],\n",
       "       [0.87628866, 0.8814433 , 0.87628866, 0.87628866, 0.87113402,\n",
       "        0.88659794, 0.87628866, 0.8814433 , 0.87628866, 0.87628866],\n",
       "       [0.86829268, 0.87317073, 0.84390244, 0.86829268, 0.86341463,\n",
       "        0.87804878, 0.86829268, 0.87317073, 0.86341463, 0.86341463],\n",
       "       [0.85253456, 0.86635945, 0.84331797, 0.86175115, 0.85714286,\n",
       "        0.87557604, 0.86175115, 0.86175115, 0.85714286, 0.86175115]])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 120,
     "status": "ok",
     "timestamp": 1643683926850,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "yVKVTpUWHnfz",
    "outputId": "3a1fe339-bdfb-40f9-92aa-a160c50279c3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.44      , 0.52      , 0.54166667, 0.54166667, 0.45833333,\n",
       "        0.54166667, 0.54166667, 0.54166667, 0.54166667, 0.54166667],\n",
       "       [0.56      , 0.64      , 0.70833333, 0.70833333, 0.79166667,\n",
       "        0.66666667, 0.75      , 0.75      , 0.79166667, 0.75      ]])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_scores[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 388
    },
    "executionInfo": {
     "elapsed": 314,
     "status": "ok",
     "timestamp": 1643683935304,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "DfymwBPjHnfz",
    "outputId": "1a73aaf9-01dc-425d-ad08-bea1dd6335eb"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_mean = np.mean(train_scores, axis = 1)\n",
    "train_std = np.std(train_scores, axis = 1)\n",
    "test_mean = np.mean(test_scores, axis = 1)\n",
    "test_std = np.std(test_scores, axis = 1)\n",
    "\n",
    "plt.figure(figsize = [8, 6])\n",
    "plt.plot(train_sizes, train_mean,\n",
    "    color = 'blue', marker = 'o',\n",
    "    markersize = 5, label = 'Training accuracy')\n",
    "plt.fill_between(train_sizes,\n",
    "    train_mean + train_std,\n",
    "    train_mean - train_std,\n",
    "    alpha = 0.15, color = 'blue')\n",
    "plt.plot(train_sizes, test_mean,\n",
    "    color = 'green', linestyle = '--',\n",
    "    marker = 's', markersize = 5,\n",
    "    label = 'Validation accuracy')\n",
    "plt.fill_between(train_sizes,\n",
    "    test_mean + test_std,\n",
    "    test_mean - test_std,\n",
    "    alpha = 0.15, color = 'green')\n",
    "plt.grid()\n",
    "plt.xlabel('Number of training examples')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.ylim([0.20, 1.03])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9TLZZeN-Hnfz"
   },
   "source": [
    "### Validation curve using sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "executionInfo": {
     "elapsed": 134,
     "status": "ok",
     "timestamp": 1643684002111,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "eKZh8lA5Hnf0"
   },
   "outputs": [],
   "source": [
    "dt = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1643684003008,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "Nw-2vynwHnf0",
    "outputId": "89f2e803-8745-4cd1-f41a-791d37d8503b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4,  8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68,\n",
       "       72, 76, 80])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_range = (np.linspace(X_train.shape[0] / 50, X_train.shape[0] / 3, 20)).astype(int)\n",
    "param_range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "executionInfo": {
     "elapsed": 587,
     "status": "ok",
     "timestamp": 1643684024112,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "wJjTQwGsHnf0"
   },
   "outputs": [],
   "source": [
    "train_scores, test_scores = validation_curve(\n",
    "    estimator = dt,\n",
    "    X = X_train,\n",
    "    y = y_train,\n",
    "    param_name = 'min_samples_split',\n",
    "    param_range = param_range,\n",
    "    cv = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 388
    },
    "executionInfo": {
     "elapsed": 292,
     "status": "ok",
     "timestamp": 1643684028474,
     "user": {
      "displayName": "Ilyas Ustun",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "07102234121230289250"
     },
     "user_tz": 360
    },
    "id": "zj_3thWIHnf0",
    "outputId": "bbdc0e73-701d-416c-9900-86f08314293c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_mean = np.mean(train_scores, axis = 1)\n",
    "train_std = np.std(train_scores, axis = 1)\n",
    "test_mean = np.mean(test_scores, axis = 1)\n",
    "test_std = np.std(test_scores, axis = 1)\n",
    "\n",
    "plt.figure(figsize = [8, 6])\n",
    "plt.plot(param_range, train_mean,\n",
    "    color = 'blue', marker = 'o',\n",
    "    markersize = 5, label = 'Training accuracy')\n",
    "plt.fill_between(param_range,\n",
    "    train_mean + train_std,\n",
    "    train_mean - train_std,\n",
    "    alpha = 0.15, color = 'blue')\n",
    "plt.plot(param_range, test_mean,\n",
    "    color = 'green', linestyle = '--',\n",
    "    marker = 's', markersize = 5,\n",
    "    label = 'Validation accuracy')\n",
    "plt.fill_between(param_range,\n",
    "    test_mean + test_std,\n",
    "    test_mean - test_std,\n",
    "    alpha = 0.15, color = 'green')\n",
    "plt.grid()\n",
    "plt.xlabel('Parameter')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.ylim([0.20, 1.03])\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "decision trees cross-validation gridsearch.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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}