Book Image

Machine Learning with R

By : Brett Lantz
Book Image

Machine Learning with R

By: Brett Lantz

Overview of this book

Machine learning, at its core, is concerned with transforming data into actionable knowledge. This fact makes machine learning well-suited to the present-day era of "big data" and "data science". Given the growing prominence of R—a cross-platform, zero-cost statistical programming environment—there has never been a better time to start applying machine learning. Whether you are new to data science or a veteran, machine learning with R offers a powerful set of methods for quickly and easily gaining insight from your data. "Machine Learning with R" is a practical tutorial that uses hands-on examples to step through real-world application of machine learning. Without shying away from the technical details, we will explore Machine Learning with R using clear and practical examples. Well-suited to machine learning beginners or those with experience. Explore R to find the answer to all of your questions. How can we use machine learning to transform data into action? Using practical examples, we will explore how to prepare data for analysis, choose a machine learning method, and measure the success of the process. We will learn how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data. "Machine Learning with R" will provide you with the analytical tools you need to quickly gain insight from complex data.
Table of Contents (19 chapters)
Machine Learning with R
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
9
Finding Groups of Data – Clustering with k-means
Index

Understanding naive Bayes


The basic statistical ideas necessary to understand the naive Bayes algorithm have been around for centuries. The technique descended from the work of the 18th century mathematician Thomas Bayes, who developed foundational mathematical principles (now known as Bayesian methods) for describing the probability of events, and how probabilities should be revised in light of additional information.

We'll go more in depth later, but for now it suffices to say that the probability of an event is a number between 0 percent and 100 percent that captures the chance that the event will occur given the available evidence. The lower the probability, the less likely the event is to occur. A probability of 0 percent indicates that the event definitely will not occur, while a probability of 100 percent indicates that the event certainly will occur.

Classifiers based on Bayesian methods utilize training data to calculate an observed probability of each class based on feature values...