Tree-based Machine Learning Algorithms: Decision Trees, Random Forests, and Boosting

by Clinton Sheppard

Get a hands-on introduction to building and using decision trees and random forests. Tree-based machine learning algorithms are used to categorize data based on known outcomes in order to facilitate predicting outcomes in new situations. You will learn not only how to use decision trees and random forests for classification and regression, and some of their respective limitations, but also how the algorithms that build them work. Each chapter introduces a new data concern and then walks you through modifying the code, thus building the engine just-in-time. Along the way you will gain experience making decision trees and random forests work for you. This book uses Python, an easy to read programming language, as a medium for teaching you how these algorithms work, but it isn't about teaching you Python, or about using pre-built machine learning libraries specific to Python. It is about teaching you how some of the algorithms inside those kinds of libraries work and why we might use them, and gives you hands-on experience that you can take back to your favorite programming environment. Table of

110 pages, Paperback

Published September 9, 2017

About the author

I am a polyglot programmer with more than 15 years of professional programming experience. My personal projects include photography, playing board games, genealogy and genetic programming.