R in Action, Second Edition

Chapter 17. Classification

This chapter covers

• Classifying with decision trees
• Ensemble classification with random forests
• Creating a support vector machine
• Evaluating classification accuracy

Data analysts are frequently faced with the need to predict a categorical outcome from a set of predictor variables. Some examples include

• Predicting whether an individual will repay a loan, given their demographics and financial history
• Determining whether an ER patient is having a heart attack, based on their symptoms and vital signs
• Deciding whether an email is spam, given the presence of key words, images, hypertext, header information, and origin

Each of these cases involves the prediction of a binary categorical outcome (good credit ...