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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Index

Advanced Boosting Algorithms

In this chapter, we are going to discuss some important algorithms that exploit different estimators to improve the overall performance of an ensemble or committee. These techniques work either by introducing a medium level of randomness in every estimator belonging to a predefined set, or by creating a sequence of estimators where each new model is forced to improve the performance of the previous ones. These techniques allow us to reduce both the bias and the variance (thereby increasing validation accuracy) when employing models with a limited capacity or that are more prone to overfit the training set.

In particular, the topics covered in the chapter are as follows:

  • Gradient boosting
  • Ensembles of voting classifiers, stacking, and bucketing

We can now start the exploration of the main concepts related to gradient boosting, which is an extremely flexible model that exploits both the simplicity of simpler algorithms (like...