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

Ensemble learning fundamentals

The main concept behind ensemble learning is the distinction between strong and weak learners. In particular, a strong learner is a classifier or a regressor which has enough capacity to reach the highest potential accuracy, minimizing both bias and variance (thus also achieving a satisfactory level of generalization).

On the other hand, a weak learner is a model that is generically able to achieve an accuracy slightly higher than a random guess, but whose complexity is very low (they can be trained very quickly but can never be used alone to solve complex problems).

To define a strong learner more formally, if we consider a parametrized binary classifier , we define it as a strong learner if the following is true:

This expression can initially appear cryptic; however, it's very easy to understand. It simply expresses the concept that a strong learner is theoretically able to achieve any non-null probability of misclassification with...