R in Action, Third Edition

17 Classification

This chapter covers

• Classifying with decision trees
• Building a random forest classifier
• Creating a support vector machine
• Evaluating classification accuracy
• Understanding complex models

Data analysts frequently 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 risk/bad credit ...