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

Ensemble learning as model selection

This is not a proper ensemble learning technique, but it is sometimes known as bucketing. In the previous section, we have discussed how a few strong learners with different peculiarities can be employed to make up a committee.

However, in many cases, a single learner is enough to achieve a good bias-variance trade-off, but it's not so easy to choose among the whole machine learning algorithm population. For this reason, when a family of similar problems must be solved (they can differ but it's better to consider scenarios that can be easily compared), it's possible to create an ensemble containing several models and use cross-validation to find the one whose performances are the best. At the end of the process, a single learner will be used, but its choice can be considered like a grid search with a voting system.

Sometimes, this technique can unveil important differences even using similar datasets. For example...