intial commit (forked from private repo)

extracted_cells/cell15.py

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+# Hyperparameter-Grid definieren
+param_grid = {
+    'n_estimators': [50, 100],
+    'max_depth': [None, 10, 20],
+    'min_samples_split': [2, 5],
+    'min_samples_leaf': [1, 2]
+}
+
+# GridSearchCV
+grid_search = GridSearchCV(RandomForestClassifier(random_state=42), param_grid, cv=3, scoring='accuracy')
+grid_search.fit(X_train, y_train)
+
+# Beste Parameter
+print("Beste Parameter:")
+print(grid_search.best_params_)
+
+# Bestes Modell
+best_rf_model = grid_search.best_estimator_
+
+# Vorhersagen mit dem besten Modell
+y_pred_best = best_rf_model.predict(X_test)
+
+# Modellleistung evaluieren
+accuracy_best = accuracy_score(y_test, y_pred_best)
+precision_best = precision_score(y_test, y_pred_best)
+recall_best = recall_score(y_test, y_pred_best)
+f1_best = f1_score(y_test, y_pred_best)
+
+print(f"\nBestes Modell:")
+print(f"Accuracy: {accuracy_best:.4f}")
+print(f"Precision: {precision_best:.4f}")
+print(f"Recall: {recall_best:.4f}")
+print(f"F1 Score: {f1_best:.4f}")