Ensemble Methods and Tree-Based Models

45 lessons in AI Fundamentals

• Out-of-Bag ErrorQuiz
• Feature Randomness in ForestsQuiz
• Tree Depth and OverfittingQuiz
• AdaBoost MechanicsQuiz
• Modern Gradient Boosting LibrariesQuiz
• Gradient Boosting MechanicsQuiz
• Feature ImportanceQuiz
• Regression TreesQuiz
• Tree PruningQuiz
• Regularization in Gradient BoostingQuiz
• Bagging ConceptQuiz
• Key Tree HyperparametersQuiz
• Decision Tree StructureQuiz
• Bagging vs BoostingQuiz
• Overfitting in Tree EnsemblesQuiz
• Learning Rate in BoostingQuiz
• Hard vs Soft VotingQuiz
• Boosting ConceptQuiz
• Stacking and BlendingQuiz
• Information GainQuiz
• Bias-Variance and EnsemblesQuiz
• Gini and EntropyQuiz
• What Ensemble Learning IsQuiz
• Random Forest MechanicsQuiz
• Trees vs Ensembles InterpretabilityQuiz
• Weak LearnersQuiz
• Bootstrap SamplingQuiz
• AdaBoost MechanicsSlides / Video
• Boosting and Sequential Error CorrectionSlides / Video
• Out-of-Bag Error EstimationSlides / Video
• Random Forests: Trees Plus RandomnessSlides / Video
• Bagging (Bootstrap Aggregating) ExplainedSlides / Video
• What Ensemble Learning IsSlides / Video
• Advantages and Limits of Decision TreesSlides / Video
• Decision Trees: Regression vs ClassificationSlides / Video
• Tree Depth, Pruning, and OverfittingSlides / Video
• Gini Impurity vs Entropy SplitsSlides / Video
• Decision Tree Structure BasicsSlides / Video
• Feature Importance from TreesSlides / Video
• Key Hyperparameters for Tree EnsemblesSlides / Video
• Voting Ensembles: Hard vs Soft VotingSlides / Video
• Stacking and Blending EnsemblesSlides / Video
• Bagging vs Boosting Trade-offsSlides / Video
• XGBoost, LightGBM, CatBoost OverviewSlides / Video
• Gradient Boosting Machines ExplainedSlides / Video