#### 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.
Machine Learning with R - Third Edition
Contributors
Preface
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Introducing Machine Learning
Managing and Understanding Data
Lazy Learning – Classification Using Nearest Neighbors
Probabilistic Learning – Classification Using Naive Bayes
Divide and Conquer – Classification Using Decision Trees and Rules
Forecasting Numeric Data – Regression Methods
Black Box Methods – Neural Networks and Support Vector Machines
Finding Patterns – Market Basket Analysis Using Association Rules
Finding Groups of Data – Clustering with k-means
Evaluating Model Performance
Improving Model Performance
Specialized Machine Learning Topics
Index

## Understanding Naive Bayes

The basic statistical ideas necessary to understand the Naive Bayes algorithm have existed for centuries. The technique descended from the work of the 18th century mathematician Thomas Bayes, who developed foundational principles for describing the probability of events and how probabilities should be revised in light of additional information. These principles formed the foundation for what are now known as Bayesian methods.

We will cover these methods in greater detail later on. For now, it suffices to say that a probability is a number between zero and one (that is, from zero to 100 percent), which captures the chance that an event will occur in light of the available evidence. The lower the probability, the less likely the event is to occur. A probability of zero indicates that the event will definitely not occur, while a probability of one indicates that the event will occur with absolute certainty.

Classifiers based on Bayesian methods utilize training data...