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

AdaBoost

In the previous section, we saw that sampling with a replacement leads to datasets where the data points are randomly reweighted. However, if the sample size M is very large, most of the points will appear only once and, moreover, all the choices will be totally random. AdaBoost is an algorithm proposed by Schapire and Freund that tries to maximize the efficiency of each weak learner by employing adaptive boosting (the name derives from this). In particular, the ensemble is grown sequentially, and the data distribution is recomputed at each step so as to increase the weight of those points that were misclassified and reduce the weight of the ones that were correctly classified. In this way, every new learner is forced to focus on those regions that were more problematic for the previous estimators. The reader can immediately understand that, contrary to random forests and other bagging methods, boosting doesn't rely on randomness to reduce the variance and improve the...