Book Image

Mastering Machine Learning Algorithms. - Second Edition

By : Giuseppe Bonaccorso
Book Image

Mastering Machine Learning Algorithms. - Second Edition

By: Giuseppe Bonaccorso

Overview of this book

Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios.
Table of Contents (28 chapters)
26
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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...