Ensemble Learning
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Section 1: Introduction to Ensemble Models
- Introduction to Ensembles
- Types of Ensembles
Section 2: Bagging Ensembles
- Bootstrap Aggregation
- How Bagging Reduces Variance
- Random Forest Algo
- Out of Bag Cross validation
- Bias Variance Tradeoff for Random Forests
- Hyperparameters
- Warm Start
Section 3: Extremely Randomised Trees
- Extremely Randomised Trees
Section 4: Gradient Boosing Algorithm
- Boosting Main Ideas
- Boosting Intuition
- Pseudo Residuals for Gradient Boosting
- Gradient Boosting Algorithm
- Controlling the learning rate
Section 5: Adaboost
- AdaBoost Intuition and Formulaa
- Adaboost Visual Example
Section 6: Stacking Ensembles
- Stacking based ensembles
- Stacking Code Demo
Section 6: Cascading Ensembles
- Cascading Ensembles
- Cascading Models Special Cases
Section 7: Cohorted Ensembles
- Cohorted Ensembles
Section 8: XGBoost
- Extreme Gradient Boosting
- XGBoost Demo
Section 9: CatBoost
- CatBoost
Section 10: LightGBM
- LightGBM