bugfix

brancheneinstufung.py

@@ -9240,7 +9240,7 @@ class DataProcessor:
         # Feature-Liste speichern (bleibt unverändert)
         self._expected_features = feature_columns_ml 
         try:
-            patterns_data = {"feature_columns": self._expected_features, "target_classes": list(dt_classifier.classes_)}
+            patterns_data = {"feature_columns": self._expected_features, "target_classes": list(rf_classifier.classes_)} # << KORRIGIERT
             patterns_dir = os.path.dirname(patterns_out)
             if patterns_dir and not os.path.exists(patterns_dir):
                 os.makedirs(patterns_dir, exist_ok=True)
@@ -9256,26 +9256,26 @@ class DataProcessor:
 
         # 5. Evaluation (Optional, aber empfohlen, um die Modellleistung zu bewerten)
         self.logger.info("Starte Modellevaluation...")
-        y_pred = dt_classifier.predict(X_test_imputed)
+        y_pred = rf_classifier.predict(X_test_imputed) # << KORRIGIERT
         accuracy = accuracy_score(y_test, y_pred)
         self.logger.info(f"Modell Genauigkeit auf dem Testset: {accuracy:.4f}")
         
-        class_report_labels = list(dt_classifier.classes_) # Sicherstellen, dass es eine Liste ist
+        class_report_labels = list(rf_classifier.classes_) # << KORRIGIERT
         class_report = classification_report(y_test, y_pred, zero_division=0, labels=class_report_labels, target_names=[str(c) for c in class_report_labels])
         self.logger.info(f"Klassifikationsbericht auf dem Testset:\n{class_report}")
 
         cm = confusion_matrix(y_test, y_pred, labels=class_report_labels)
         self.logger.info(f"Konfusionsmatrix auf dem Testset (Zeilen=Wahr, Spalten=Vorhersage):\n{cm}")
 
+        # Block für Feature Importance
         try:
             self.logger.info("Feature Importance (Top 15):")
-            importances = rf_classifier.feature_importances_
+            importances = rf_classifier.feature_importances_ # << Hier war es schon korrekt
             feature_importance_df = pd.DataFrame({
                 'Feature': feature_columns_ml,
                 'Importance': importances
             }).sort_values(by='Importance', ascending=False)
             
-            # Ausgabe der Top 15 Features
             self.logger.info(f"\n{feature_importance_df.head(15).to_string(index=False)}")
         except Exception as e_feat_imp:
             self.logger.warning(f"FEHLER beim Berechnen/Anzeigen der Feature Importance: {e_feat_imp}")