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

Ensembles of voting classifiers

A simpler but no less effective way to create an ensemble is based on the idea of exploiting a limited number of strong learners whose peculiarities allow them to yield better performances in particular regions of the sample space. Let's start considering a set of Nc discrete-valued classifiers, . The algorithms are different, but they are all trained with the same dataset and output the same label set. The simplest strategy is based on a hard-voting approach:

In this case, the function counts the number of estimators that output the label yi. This method is rather powerful in many cases but has some limitations. If we rely only on a majority vote, we are implicitly assuming that a correct classification is obtained by a large number of estimators. Even if, votes are necessary to output a result, in many cases, their number is much higher. Moreover, when k is not very large, also votes imply a symmetry that involves a large part of the...