Book Image

Machine Learning with R - Third Edition

By : Brett Lantz
Book Image

Machine Learning with R - Third Edition

By: Brett Lantz

Overview of this book

Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data. Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings. This new 3rd edition updates the classic R data science book to R 3.6 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R.
Table of Contents (18 chapters)
Machine Learning with R - Third Edition
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Understanding regression trees and model trees

If you recall from Chapter 5, Divide and Conquer – Classification Using Decision Trees and Rules, a decision tree builds a model, much like a flowchart, in which decision nodes, leaf nodes, and branches define a series of decisions that are used to classify examples. Such trees can also be used for numeric prediction by making only small adjustments to the tree growing algorithm. In this section, we will consider the ways in which trees for numeric prediction differ from trees used for classification.

Trees for numeric prediction fall into two categories. The first, known as regression trees, were introduced in the 1980s as part of the seminal classification and regression tree (CART) algorithm. Despite the name, regression trees do not use linear regression methods as described earlier in this chapter; rather, they make predictions based on the average value of examples that reach a leaf.


The CART algorithm is described in detail in Classification...