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)
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The main concept behind ensemble learning is the distinction between strong and weak learners. In particular, a strong learner is a classifier or a regressor which has enough capacity to reach the highest potential accuracy, minimizing both bias and variance (thus also achieving a satisfactory level of generalization).

On the other hand, a weak learner is a model that is generically able to achieve an accuracy slightly higher than a random guess, but whose complexity is very low (they can be trained very quickly but can never be used alone to solve complex problems).

To define a strong learner more formally, if we consider a parametrized binary classifier , we define it as a strong learner if the following is true:

This expression can initially appear cryptic; however, it's very easy to understand. It simply expresses the concept that a strong learner is theoretically able to achieve any non-null probability of misclassification with...