Ensemble Learning for Ai Developers: Learn Bagging, Stacking, and Boosting Methods With Use Cases

Table of Contents
About the Authors
About the Technical Reviewer
Acknowledgments
Introduction
Chapter 1: Why Ensemble Techniques Are Needed
	Mixing Training Data
	Mixing Combinations
	Mixing Models
	Summary
Chapter 2: Mixing Training Data
	Decision Trees
	Sampling a Dataset
		Sampling Without Replacement (WOR)
		Sampling with Replacement (WR)
	Bagging (Bootstrap Aggregating)
		k-Fold Cross-Validation
	Stratified k-Fold Cross-Validation
	Summary
Chapter 3: Mixing Models
	Voting Ensembles
		Hard Voting
		Averaging/Soft Voting
		Hyperparameter Tuning Ensembles
		Horizontal Voting Ensembles
		Snapshot Ensembles
	Summary
Chapter 4: Mixing Combinations
	Boosting
	AdaBoost
	Gradient Boosting
	XGBoost
	Stacking
	Summary
Chapter 5: Using Ensemble Learning Libraries
	ML-Ensemble
		Multilayer Ensembles
		Ensemble Model Selection
			The Scoring Function
			Evaluator
			Preprocessing
			Summary
	Scale XGBoost via Dask
		Dask Arrays and Dataframes
		Dask-ML
		Scaling XGBoost
		Microsoft LightGBM
			Why LightGBM Architecture Is Best
				Growing the Tree
				Finding the Best Split
					Histogram-based Methods
					Treating Missing Values
					Gradient-based One-Side Sampling
					Exclusive Feature Bundling
			Parameters
			LightGBM in Python Code
		AdaNet
	Summary
Chapter 6: Tips and Best Practices
	Feature Selection Using Random Forests
	Feature Transformations with Ensembles of Trees
	Building a Preprocessing Pipeline for a Random Forest Classifier
	Isolation Forest for Outlier Detection
	Scaling Ensembles with Dask
		Preprocessing
		Hyperparameter Search
			Drop-in Replacement for scikit-learn
			Incremental Hyperparameter Optimization
		Distributed Ensemble Fitting
	Summary
	Thank You
Index