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

Other regression techniques

A brief introduction to following regression techniques comes next, and why you may prefer to use them in comparison to least squares. In this section, we'll cover:

Ridge regression, with a practical example in scikit-learn
Lasso and logistic regression
Polynomial regression with examples
Isotonic regression

One of the most common problems with linear regression is the ill-conditioning that causes instabilities in the solution. Ridge regression has been introduced to overcome this problem.

Ridge Regression

A very common problem in regression models arises as a result of the structure of X^TX. We have previously shown that the presence of multi-collinearities forces , and this implies that the inversion becomes extremely problematic. A simple way to check the presence of multi-collinearities is based on the computation of the condition number of X^TX (with normalized columns with a length equal to 1), defined as: