ML Mastery Notes – SVM, Ensemble Methods & Boosting

Topics Covered in the Handwritten ML Notebook 1. Support Vector Machine (SVM) - Hyperplane, Margin, Support Vectors - Kernels (Linear, Polynomial, RBF, Sigmoid) - SVC (Support Vector Classification) - SVR (Support Vector Regression) - Slack variables, ε-tube, optimization 2. Ensemble Learning - Bagging - Boosting - Stacking - Voting 3. Bagging Techniques - Bagging Classifier - Bagging Regressor - Random Forest Classifier - Random Forest Regressor - Splitting Criteria (Gini, Entropy, MSE, MAE) 4. Boosting Methods - AdaBoost - Gradient Boosting (GBM) - XGBoost - LightGBM - CatBoost - Key XGBoost features (Regularization, Parallelism, Pruning) - XGBoost parameters + objective function 5. Stacking - Meta-model, level-1 data - Stacking Classifier - Stacking Regressor 6. Voting - Hard Voting - Soft Voting - Voting Classifier - Voting Regressor 7. Code Samples Included For: - Bagging Classifier & Regressor - RandomForest Classifier & Regressor - AdaBoost Classifier & Regressor - GradientBoosting Classifier & Regressor - XGBoost Classifier & Regressor - Stacking Classifier & Regressor - Voting Classifier & Regressor