Fix: MLflow nested run error and limit Random Forest parallelism in CI

Author: Dev-Kavindu

Scripts/model_training.py

@@ -469,6 +469,9 @@ def register_model_to_registry(self, model_name: str, model, metrics: ModelMetri
         registry_name = registry_name or model_name
 
         try:
+            # End any active MLflow run before starting a new one
+            if self.mlflow.active_run() is not None:
+                self.mlflow.end_run()
             # Start MLflow run
             with self.mlflow.start_run(run_name=f"{model_name}_registration") as run:
                 # Log model parameters
@@ -1299,7 +1302,7 @@ def main():
         'min_samples_split': lambda trial: trial.suggest_int('min_samples_split', 100, 200), # High split requirement
         'min_samples_leaf': lambda trial: trial.suggest_int('min_samples_leaf', 50, 100),    # Min 50 for robust leaves
         'criterion': lambda trial: trial.suggest_categorical('criterion', ['gini', 'entropy']),
-        'n_jobs': lambda trial: -1,  # Use all CPU cores
+        'n_jobs': (lambda trial: 1) if is_ci else (lambda trial: -1),  # Limit parallelism in CI
         'class_weight': lambda trial: 'balanced'  # Handle class imbalance
     }