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
Other Books You May Enjoy
Leave a review - let other readers know what you think

Understanding decision trees

Decision tree learners are powerful classifiers that utilize a tree structure to model the relationships among the features and the potential outcomes. As illustrated in the following figure, this structure earned its name due to the fact that it mirrors the way a literal tree begins at a wide trunk and splits into narrower and narrower branches as it is followed upward. In much the same way, a decision tree classifier uses a structure of branching decisions that channel examples into a final predicted class value.

To better understand how this works in practice, let's consider the following tree, which predicts whether a job offer should be accepted. A job offer under consideration begins at the root node, where it is then passed through decision nodes that require choices to be made based on the attributes of the job. These choices split the data across branches that indicate potential outcomes of a decision. They are depicted here as yes or no outcomes, but...