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
Escuela, estudio y materia
- Institución
- Machine learning
- Grado
- Machine learning
Información del documento
- Subido en
- 9 de marzo de 2026
- Número de páginas
- 24
- Escrito en
- 2025/2026
- Tipo
- OTRO
- Personaje
- Desconocido
Temas
-
deeplearning
-
machinelearning
-
datascience
-
artificialintelligence
-
ensemblelearning
-
svm
-
xgboost
-
handwrittennotes
-
datascientist