Ensemble Learning

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Section 1: Introduction to Ensemble Models

  1. Introduction to Ensembles
  2. Types of Ensembles

Section 2: Bagging Ensembles

  1. Bootstrap Aggregation
  2. How Bagging Reduces Variance
  3. Random Forest Algo
  4. Out of Bag Cross validation
  5. Bias Variance Tradeoff for Random Forests
  6. Hyperparameters
  7. Warm Start

Section 3: Extremely Randomised Trees

  1. Extremely Randomised Trees

Section 4: Gradient Boosing Algorithm

  1. Boosting Main Ideas
  2. Boosting Intuition
  3. Pseudo Residuals for Gradient Boosting
  4. Gradient Boosting Algorithm
  5. Controlling the learning rate

Section 5: Adaboost

  1. AdaBoost Intuition and Formulaa
  2. Adaboost Visual Example

Section 6: Stacking Ensembles

  1. Stacking based ensembles
  2. Stacking Code Demo

Section 6: Cascading Ensembles

  1. Cascading Ensembles
  2. Cascading Models Special Cases

Section 7: Cohorted Ensembles

  1. Cohorted Ensembles

Section 8: XGBoost

  1. Extreme Gradient Boosting
  2. XGBoost Demo

Section 9: CatBoost

  1. CatBoost

Section 10: LightGBM

  1. LightGBM
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