Ensemble Methods for Machine Learning

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

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.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

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

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 Hype or hallelujah?
PART 2 – ESSENTIAL ENSEMBLE METHODS
2 Homogeneous parallel Bagging and random forests
3 Heterogeneous parallel Combining strong learners
4 Sequential Adaptive boosting
5 Sequential Gradient boosting
6 Sequential Newton boosting
PART 3 – ENSEMBLES IN THE ADAPTING ENSEMBLE METHODS TO YOUR DATA
7 Learning with continuous and count labels
8 Learning with categorical features
9 Explaining your ensembles

Size: A4(20*28cm)

Printing: 80 gm – color

Cover: Softcover