You're reading from Mastering Machine Learning Algorithms Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work

Product type Paperback

Published in Jan 2020

Publisher Packt

ISBN-13 9781838820299

Length 798 pages

Edition 2nd Edition

Languages

Python

Tools

Keras

Concepts

Machine Learning

Authors (2):

Giuseppe Bonaccorso

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Table of Contents (28) Chapters

Preface

1. Machine Learning Model Fundamentals

2. Loss Functions and Regularization FREE CHAPTER

3. Introduction to Semi-Supervised Learning

4. Advanced Semi-Supervised Classification

5. Graph-Based Semi-Supervised Learning

6. Clustering and Unsupervised Models

7. Advanced Clustering and Unsupervised Models

8. Clustering and Unsupervised Models for Marketing

9. Generalized Linear Models and Regression

10. Introduction to Time-Series Analysis

11. Bayesian Networks and Hidden Markov Models

12. The EM Algorithm

13. Component Analysis and Dimensionality Reduction

14. Hebbian Learning

15. Fundamentals of Ensemble Learning

16. Advanced Boosting Algorithms

17. Modeling Neural Networks

18. Optimizing Neural Networks

19. Deep Convolutional Networks

20. Recurrent Neural Networks

21. Autoencoders

22. Introduction to Generative Adversarial Networks

23. Deep Belief Networks

24. Introduction to Reinforcement Learning

25. Advanced Policy Estimation Algorithms

26. Other Books You May Enjoy

27. Index

Summary

In this chapter, we introduced the main concepts of ensemble learning, focusing on both bagging and boosting techniques. In the first section, we explained the difference between strong and weak learners, and we presented the big picture of how it's possible to combine the estimators to achieve specific goals.

The next topic focused on the properties of decision trees and their main strengths and weaknesses. In particular, we explained that the structure of a tree causes a natural increase in the variance. The bagging technique called random forests allow mitigating this problem, improving at the same time the overall accuracy. A further variance reduction can be achieved by increasing the randomness and employing a variant called extra randomized trees. In the example, we have also seen how it's possible to evaluate the importance of each input feature and perform dimensionality reduction without involving complex statistical techniques.

In the third section...