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 extended the concept of ensemble learning to a generic forward stage-wise additive model, where the task of each new estimator is to minimize a generic cost function. Considering the complexity of a full optimization, a gradient descent technique was presented that, combined with an estimator weight line search, can yield excellent performances, both in classification and in regression problems.

The remainder of the chapter covered how to build ensembles using a few strong learners, averaging their prediction or considering a majority vote. We discussed the main drawback of thresholded classifiers, and we showed how it's possible to build a soft-voting model that is able to trust the estimator that shows less uncertainty. Other useful topics are the stacking method, which consists of using an extra classifier to process the prediction of each member of the ensemble and how it's possible to create candidate ensembles that are evaluated...