Section 4.5.0 - Basic Writeups
Section 4.5.0.1 - Part 1 - Wikipedia - Ensemble Learning
Section 4.5.0.2 - Part 2 - Becoming Human.ai - Ensemble Learning: Bagging and Boosting
Section 4.5.1 - Bootstrapping (Statistics)
Section 4.5.2 - Bootstrap aggregating(Bagging)
Section 4.5.2.0 - Introductory Writeups
Section 4.5.2.0.1 - Basic Writeup - Wikipedia - Bootstrap Aggregating (Bagging)
Section 4.5.2.0.2 - Detailed Writeup - Buhlmann,Yu - Explaining Bagging
Section 4.5.2.1 - Bagging Applications
Section 4.5.2.1.1 - Random Forest (Random Decision Forest)
Section 4.5.2.1.2 - Random Subspace Method (Attribute or Feature Bagging)
Section 4.5.3 - Boosting(meta-algorithm)
Section 4.5.3.0 - Basic Writeup - Wikipedia - Boosting
Section 4.5.3.1 - Adaptive Boost (AdaBoost)
Section 4.5.3.1.0 - Basic Writeup - Wikipedia - AdaBoost
Section 4.5.3.1.1 - LogitBoost
Section 4.5.3.2 - Gradient Boost
Section 4.5.3.2.0 - Basic Writeups
Section 4.5.3.2.0.1 - Part 1 - Wikipedia - Gradient Boosting
Section 4.5.3.2.0.2 - Part 2 - Jason Brownlee - A gentle introduction to the Gradient Boosting Algorithm for ML
Section 4.5.3.2.0.3 - Part 3 - Leo Breiman - Prediction Games and Arcing(Adaptive Reweighting and Combining) Algorithms - A statistical framework
Section 4.5.3.2.1 - XGBoost
Section 4.5.3.2.2 - Accelerated Gradient Boosting
Section 4.5.3.2.3 - Stochastic Gradient Boosting
Section 4.5.3.3 - Linear Programming Boost (LPBoost)
Section 4.5.3.4 - Total Boost
Section 4.5.3.5 - Brown Boost
Section 4.5.3.6 - MadaBoost
Section 4.5.3.7 - CoBoosting
Section 4.5.3.8 - RankBoost
Section 4.5.3.9 - Boosting Applications
Section 4.5.3.9.1 - Gradient Boosted Trees
Section 4.5.3.9.1.0 - Basic Writeup - Tianqi Chen - Introduction to Boosted Trees
Section 4.5.3.9.1.1 - Gradient Boosted Decision Trees
Section 4.5.3.9.1.1.0 - Basic Writeup - Flynn Wang - An Introduction to Gradient Boosted Decision Trees and XGBoost
Section 4.5.3.9.1.1.1 - Multi-layered Gradient Boosted Decision Tree
Section 4.5.3.9.1.2 - Gradient Boosted Regression Trees
Section 4.5.3.9.2 - Gradient Boosted Machine
Section 4.5.4 - Bayes optimal classifier
Section 4.5.5 - Bayesian parameter averaging
Section 4.5.6 - Bayesian model(classifier) combination
Section 4.5.7 - Bucket of models
Section 4.5.8 - Stacking
