Ensemble machine learning combines the power of multiple machine learning approaches, working together to deliver models that are highly performant and highly accurate.

Inside

Ensemble Methods for Machine Learning

you will find:

Methods for classification, regression, and recommendations

Sophisticated off-the-shelf ensemble implementations

Random forests, boosting, and gradient boosting

Feature engineering and ensemble diversity

Interpretability and explainability for ensemble methods

Ensemble machine learning trains a diverse group of machine learning models to work together, aggregating their output to deliver richer results than a single model. Now in

Ensemble Methods for Machine Learning

you’ll discover core ensemble methods that have proven records in both data science competitions and real-world applications. Hands-on case studies show you how each algorithm works in production. By the time you're done, you'll know the benefits, limitations, and practical methods of applying ensemble machine learning to real-world data, and be ready to build more explainable ML systems.

About the Technology

Automatically compare, contrast, and blend the output from multiple models to squeeze the best results from your data. Ensemble machine learning applies a “wisdom of crowds” method that dodges the inaccuracies and limitations of a single model. By basing responses on multiple perspectives, this innovative approach can deliver robust predictions even without massive datasets.

About the Book

Ensemble Methods for Machine Learning

teaches you practical techniques for applying multiple ML approaches simultaneously. Each chapter contains a unique case study that demonstrates a fully functional ensemble method, with examples including medical diagnosis, sentiment analysis, handwriting classification, and more. There’s no complex math or theory—you’ll learn in a visuals-first manner, with ample code for easy experimentation!

What’s Inside

Bagging, boosting, and gradient boosting

Methods for classification, regression, and retrieval

Interpretability and explainability for ensemble methods

Feature engineering and ensemble diversity

About the Reader

For Python programmers with machine learning experience.

About the Author

Gautam Kunapuli has over 15 years of experience in academia and the machine learning industry.

Table of Contents

PART 1 - THE BASICS OF ENSEMBLES

1 Ensemble methods: Hype or hallelujah?

PART 2 - ESSENTIAL ENSEMBLE METHODS

2 Homogeneous parallel ensembles: Bagging and random forests

3 Heterogeneous parallel ensembles: Combining strong learners

4 Sequential ensembles: Adaptive boosting

5 Sequential ensembles: Gradient boosting

6 Sequential ensembles: Newton boosting

PART 3 - ENSEMBLES IN THE WILD: ADAPTING ENSEMBLE METHODS TO YOUR DATA

7 Learning with continuous and count labels

8 Learning with categorical features

9 Explaining your ensembles

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