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ML-Kurs-SS2023/notebooks/04_decision_trees_ex_1_compare_tree_classifiers.ipynb

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added missing notebooks
2023-04-16 15:34:52 +02:00
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Logistic regression with scikit-learn: heart disease data set"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Read data "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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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",
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" <td>0.8</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <td>57</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>140</td>\n",
" <td>241</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>123</td>\n",
" <td>1</td>\n",
" <td>0.2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
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" <td>264</td>\n",
" <td>0</td>\n",
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" <td>132</td>\n",
" <td>0</td>\n",
" <td>1.2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
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" <td>193</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>141</td>\n",
" <td>0</td>\n",
" <td>3.4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>1</td>\n",
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" <td>1</td>\n",
" <td>1.2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
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" <td>2</td>\n",
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" </tbody>\n",
"</table>\n",
"<p>303 rows × 14 columns</p>\n",
"</div>"
],
"text/plain": [
" age sex cp trestbps chol fbs restecg thalach exang oldpeak \\\n",
"0 63 1 3 145 233 1 0 150 0 2.3 \n",
"1 37 1 2 130 250 0 1 187 0 3.5 \n",
"2 41 0 1 130 204 0 0 172 0 1.4 \n",
"3 56 1 1 120 236 0 1 178 0 0.8 \n",
"4 57 0 0 120 354 0 1 163 1 0.6 \n",
".. ... ... .. ... ... ... ... ... ... ... \n",
"298 57 0 0 140 241 0 1 123 1 0.2 \n",
"299 45 1 3 110 264 0 1 132 0 1.2 \n",
"300 68 1 0 144 193 1 1 141 0 3.4 \n",
"301 57 1 0 130 131 0 1 115 1 1.2 \n",
"302 57 0 1 130 236 0 0 174 0 0.0 \n",
"\n",
" slope ca thal target \n",
"0 0 0 1 1 \n",
"1 0 0 2 1 \n",
"2 2 0 2 1 \n",
"3 2 0 2 1 \n",
"4 2 0 2 1 \n",
".. ... .. ... ... \n",
"298 1 0 3 0 \n",
"299 1 0 3 0 \n",
"300 1 2 3 0 \n",
"301 1 1 3 0 \n",
"302 1 1 2 0 \n",
"\n",
"[303 rows x 14 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"filename = \"https://www.physi.uni-heidelberg.de/~reygers/lectures/2021/ml/data/heart.csv\"\n",
"df = pd.read_csv(filename)\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"y = df['target'].values\n",
"X = df[[col for col in df.columns if col!=\"target\"]]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, shuffle=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fit the model"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.ensemble import AdaBoostClassifier\n",
"from sklearn.ensemble import GradientBoostingClassifier\n",
"\n",
"lr = LogisticRegression(penalty='none', fit_intercept=True, max_iter=5000, tol=1E-5)\n",
"rf = RandomForestClassifier(max_depth=3)\n",
"ab = AdaBoostClassifier()\n",
"gb = GradientBoostingClassifier()\n",
"\n",
"classifiers = [lr, rf, ab, gb]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LogisticRegression\n",
"RandomForestClassifier\n",
"AdaBoostClassifier\n",
"GradientBoostingClassifier\n"
]
}
],
"source": [
"for clf in classifiers:\n",
" print(clf.__class__.__name__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Train models and compare ROC curves"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"### Your code here ###"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
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"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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