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 regression


Regression is concerned with specifying the relationship between a single numeric dependent variable (the value to be predicted) and one or more numeric independent variables (the predictors). We'll begin by assuming that the relationship between the independent and dependent variables follows a straight line.

Tip

The origin of the term "regression" to describe the process of fitting lines to data is rooted in a study of genetics by Sir Francis Galton in the late 19th century. Galton discovered that fathers that were extremely short or extremely tall tended to have sons whose heights were closer to average. He called this phenomenon "regression to the mean".

You might recall from algebra that lines can be defined in a slope-intercept form similar to y = a + bx, where y is the dependent variable and x is the independent variable. In this formula, the slope b indicates how much the line rises for each increase in x. The variable a indicates the value of y when x = 0...