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ML-For-Beginners/translations/de/4-Classification/3-Classifiers-2/solution/notebook.ipynb

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{
"cells": [
{
"source": [
"# Weitere Klassifikationsmodelle erstellen\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Übersicht über den Datensatz\n",
"Dieser Datensatz enthält einzelne Proben (zum Beispiel Rezepte), die nach Küchenart gekennzeichnet sind. \n",
"Jede Zeile entspricht einer einzelnen Probe/einem Datensatz, und die Spalten repräsentieren Zutaten oder andere Attribute, die für die Klassifizierung verwendet werden, einschließlich des Labels `cuisine`.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"# Load dataset containing cuisine features\n",
"cuisines_df = pd.read_csv(\"../../data/cleaned_cuisines.csv\")\n",
"cuisines_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0 indian\n",
"1 indian\n",
"2 indian\n",
"3 indian\n",
"4 indian\n",
"Name: cuisine, dtype: object"
]
},
"metadata": {},
"execution_count": 2
}
],
"source": [
"cuisines_label_df = cuisines_df['cuisine']\n",
"cuisines_label_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" almond angelica anise anise_seed apple apple_brandy apricot \\\n",
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" armagnac artemisia artichoke ... whiskey white_bread white_wine \\\n",
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"\n",
" whole_grain_wheat_flour wine wood yam yeast yogurt zucchini \n",
"0 0 0 0 0 0 0 0 \n",
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"\n",
"[5 rows x 380 columns]"
],
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>almond</th>\n <th>angelica</th>\n <th>anise</th>\n <th>anise_seed</th>\n <th>apple</th>\n <th>apple_brandy</th>\n <th>apricot</th>\n <th>armagnac</th>\n <th>artemisia</th>\n <th>artichoke</th>\n <th>...</th>\n <th>whiskey</th>\n <th>white_bread</th>\n <th>white_wine</th>\n <th>whole_grain_wheat_flour</th>\n <th>wine</th>\n <th>wood</th>\n <th>yam</th>\n <th>yeast</th>\n <th>yogurt</th>\n <th>zucchini</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
},
"metadata": {},
"execution_count": 3
}
],
"source": [
"cuisines_features_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n",
"cuisines_features_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Verschiedene Klassifikatoren ausprobieren\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.svm import SVC\n",
"from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
"from sklearn.model_selection import train_test_split, cross_val_score\n",
"from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(cuisines_features_df, cuisines_label_df, test_size=0.3)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"\n",
"C = 10\n",
"# Create different classifiers.\n",
"classifiers = {\n",
" 'Linear SVC': SVC(kernel='linear', C=C, probability=True,random_state=0),\n",
" 'KNN classifier': KNeighborsClassifier(C),\n",
" 'SVC': SVC(),\n",
" 'RFST': RandomForestClassifier(n_estimators=100),\n",
" 'ADA': AdaBoostClassifier(n_estimators=100)\n",
" \n",
"}\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Accuracy (train) for Linear SVC: 76.4% \n",
" precision recall f1-score support\n",
"\n",
" chinese 0.64 0.66 0.65 242\n",
" indian 0.91 0.86 0.89 236\n",
" japanese 0.72 0.73 0.73 245\n",
" korean 0.83 0.75 0.79 234\n",
" thai 0.75 0.82 0.78 242\n",
"\n",
" accuracy 0.76 1199\n",
" macro avg 0.77 0.76 0.77 1199\n",
"weighted avg 0.77 0.76 0.77 1199\n",
"\n",
"Accuracy (train) for KNN classifier: 70.7% \n",
" precision recall f1-score support\n",
"\n",
" chinese 0.65 0.63 0.64 242\n",
" indian 0.84 0.81 0.82 236\n",
" japanese 0.60 0.81 0.69 245\n",
" korean 0.89 0.53 0.67 234\n",
" thai 0.69 0.75 0.72 242\n",
"\n",
" accuracy 0.71 1199\n",
" macro avg 0.73 0.71 0.71 1199\n",
"weighted avg 0.73 0.71 0.71 1199\n",
"\n",
"Accuracy (train) for SVC: 80.1% \n",
" precision recall f1-score support\n",
"\n",
" chinese 0.71 0.69 0.70 242\n",
" indian 0.92 0.92 0.92 236\n",
" japanese 0.77 0.78 0.77 245\n",
" korean 0.87 0.77 0.82 234\n",
" thai 0.75 0.86 0.80 242\n",
"\n",
" accuracy 0.80 1199\n",
" macro avg 0.80 0.80 0.80 1199\n",
"weighted avg 0.80 0.80 0.80 1199\n",
"\n",
"Accuracy (train) for RFST: 82.8% \n",
" precision recall f1-score support\n",
"\n",
" chinese 0.80 0.75 0.77 242\n",
" indian 0.90 0.91 0.90 236\n",
" japanese 0.82 0.78 0.80 245\n",
" korean 0.85 0.82 0.83 234\n",
" thai 0.78 0.89 0.83 242\n",
"\n",
" accuracy 0.83 1199\n",
" macro avg 0.83 0.83 0.83 1199\n",
"weighted avg 0.83 0.83 0.83 1199\n",
"\n",
"Accuracy (train) for ADA: 71.1% \n",
" precision recall f1-score support\n",
"\n",
" chinese 0.60 0.57 0.58 242\n",
" indian 0.87 0.84 0.86 236\n",
" japanese 0.71 0.60 0.65 245\n",
" korean 0.68 0.78 0.72 234\n",
" thai 0.70 0.78 0.74 242\n",
"\n",
" accuracy 0.71 1199\n",
" macro avg 0.71 0.71 0.71 1199\n",
"weighted avg 0.71 0.71 0.71 1199\n",
"\n"
]
}
],
"source": [
"n_classifiers = len(classifiers)\n",
"\n",
"for index, (name, classifier) in enumerate(classifiers.items()):\n",
" classifier.fit(X_train, np.ravel(y_train))\n",
"\n",
" y_pred = classifier.predict(X_test)\n",
" accuracy = accuracy_score(y_test, y_pred)\n",
" print(\"Accuracy (train) for %s: %0.1f%% \" % (name, accuracy * 100))\n",
" print(classification_report(y_test,y_pred))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n\n<!-- CO-OP TRANSLATOR DISCLAIMER START -->\n**Haftungsausschluss**: \nDieses Dokument wurde mit dem KI-Übersetzungsdienst [Co-op Translator](https://github.com/Azure/co-op-translator) übersetzt. Zwar bemühen wir uns um Genauigkeit, bitte beachten Sie jedoch, dass automatisierte Übersetzungen Fehler oder Ungenauigkeiten enthalten können. Das Originaldokument in seiner Ursprungssprache ist als maßgebliche Quelle zu betrachten. Für wichtige Informationen wird eine professionelle menschliche Übersetzung empfohlen. Wir übernehmen keine Haftung für Missverständnisse oder Fehlinterpretationen, die aus der Nutzung dieser Übersetzung entstehen.\n<!-- CO-OP TRANSLATOR DISCLAIMER END -->\n"
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