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

Gradient boosting

At this point, we can introduce a more general method of creating boosted ensembles. Let's choose a generic algorithm family, represented as follows:

Each model is parametrized using the vector and there are no restrictions on the kind of method that is employed. In this case, we are going to consider decision trees (which is one of the most diffused algorithms when this boosting strategy is employed—in this case, the algorithm is known as gradient tree boosting), but the theory is generic and can be easily applied to more complex models, such as neural networks. In a decision tree, the parameter vector is made up of selection tuples, so the reader can think of this method as a pseudo-random forest where, instead of randomness, we look for extra optimality exploiting the previous experience. In fact, as with AdaBoost, a gradient boosting ensemble is built sequentially, using a technique that is formally defined as Forward Stage-wise Additive Modeling...