Ensemble Methods for Machine Learning

8 Learning with categorical features

This chapter covers

• Introducing categorical features in machine learning
• Preprocessing categorical features using supervised and unsupervised encoding
• Understanding ordered boosting
• Using CatBoost for categorical variables
• Handling high-cardinality categorical features

Data sets for supervised machine learning consist of features that describe objects and labels that describe the targets we’re interested in modeling. At a high level, features, also known as attributes or variables, are usually classified into two types: continuous and categorical.

A categorical feature is one that takes a discrete value from a set of finite, nonnumeric values, called categories. Categorical features are ubiquitous and appear ...