Model-training.md

@@ -32,6 +32,9 @@
     - **Random Forest:** Number of trees (n_estimators), maximum depth (max_depth), and minimum samples for splits.
     - **Logistic Regression:** Regularization strength (C) and solver.
     - **kNN:** Number of neighbors (n_neighbors) and distance metric.
+    - **Ensemble Model (SVM + Random Forest):** Combines the strengths of both SVM and Random Forest to improve the accuracy and robustness of predictions.
+     - The ensemble method uses **voting** or **stacking** to combine the individual predictions from SVM and Random Forest.
+     - **Voting Classifier:** Predicts the class by taking a majority vote from both SVM and Random Forest predictions.
 
 ### Step 4: Model Evaluation
 - After training, models are evaluated using multiple metrics: