Book Image

Data Analysis with R, Second Edition - Second Edition

Book Image

Data Analysis with R, Second Edition - Second Edition

Overview of this book

Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. Starting with the basics of R and statistical reasoning, this book dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples. Packed with engaging problems and exercises, this book begins with a review of R and its syntax with packages like Rcpp, ggplot2, and dplyr. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with messy data, large data, communicating results, and facilitating reproducibility. This book is engineered to be an invaluable resource through many stages of anyone’s career as a data analyst.
Table of Contents (24 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Index

Chapter 10. Predicting Categorical Variables

Our first foray into predictive analytics began with regression techniques for predicting continuous variables. In this chapter, we will be discussing a perhaps even more popular class of techniques from statistical learning known as classification.

All these techniques have at least one thing in common: we train a learner on input, for which the correct classifications are known, with the intention of using the trained model on new data whose class is unknown. In this way, classification is a set of algorithms and methods to predict categorical variables.

Whether you know it or not, statistical learning algorithms performing classification are all around you. For example, if you've ever accidently checked the spam folder of your email and been horrified, you can thank your lucky stars that there are sophisticated classification mechanisms that your email is run through to automatically mark spam as such so you don't have to see it. On the other...