Model-training.md

@@ -24,6 +24,10 @@
   2. **Random Forest (RF):** A robust ensemble method that aggregates the predictions of multiple decision trees, ensuring better generalization.
   3. **Logistic Regression:** A fundamental method for binary classification that provides a clear probability-based output.
   4. **k-Nearest Neighbors (kNN):** A non-parametric model that predicts based on the closest neighbors, effective for classification tasks.
+  5. **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.
   
 - **Hyperparameter Tuning:**
   - **GridSearchCV:** A search over a specified parameter grid is used to identify the best hyperparameters for each model.
@@ -32,9 +36,7 @@
     - **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.
+    - **Ensemble Model:** Hyperparameters for both SVM and Random Forest models are optimized separately before being combined in the ensemble method.
 
 ### Step 4: Model Evaluation
 - After training, models are evaluated using multiple metrics: