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klara 2024-06-21 17:24:25 +02:00
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commit 6ad5a5ec25
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notebooks/decision_tree.ipynb
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@ -144,24 +144,23 @@
} }
], ],
"source": [ "source": [
"# Schritt 1: Importiere die notwendigen Bibliotheken\n", "\n",
"from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.metrics import accuracy_score\n", "from sklearn.metrics import accuracy_score\n",
"\n", "\n",
"# Schritt 2: Erstelle das Decision Tree Modell\n", "\n",
"# Beispiel: Begrenzung der Tiefe des Baumes\n", "# Beispiel: Begrenzung der Tiefe des Baumes\n",
"dt_classifier = DecisionTreeClassifier(max_depth=5)\n", "dt_classifier = DecisionTreeClassifier(max_depth=5)\n",
"\n", "\n",
"# Schritt 3: Trainiere das Modell\n", "# Schritt 3: Trainieren des Modells mit Trainingsdaten\n",
"dt_classifier.fit(train_x, train_y)\n", "dt_classifier.fit(train_x, train_y)\n",
"\n", "\n",
"# Schritt 4: Vorhersagen und Auswertung auf den Validierungsdaten\n", "# Schritt 4: Bewertung des Modells mit Validierungsdaten\n",
"valid_pred = dt_classifier.predict(valid_x)\n", "valid_pred = dt_classifier.predict(valid_x)\n",
"valid_accuracy = accuracy_score(valid_y, valid_pred)\n", "valid_accuracy = accuracy_score(valid_y, valid_pred)\n",
"print(f'Validierungsgenauigkeit: {valid_accuracy}')\n", "print(f'Validierungsgenauigkeit: {valid_accuracy}')\n",
"\n", "\n",
"# Optional: Tuning des Modells basierend auf den Validierungsergebnissen\n", "# Schritt 5: Hyperparameter-Optimierung\n",
"# Experimentiere mit verschiedenen Parametern und trainiere bei Bedarf neu\n",
"\n", "\n",
"# Schritt 6: Endgültige Bewertung mit Testdaten\n", "# Schritt 6: Endgültige Bewertung mit Testdaten\n",
"test_pred = dt_classifier.predict(test_x)\n", "test_pred = dt_classifier.predict(test_x)\n",