In the last chapter, we briefly mentioned one-hot encoding, which transforms categorical features to numerical features in order to be used in the tree-based algorithms in scikit-learn. It will not limit our choice to tree-based algorithms if we can adopt this technique to other algorithms that only take in numerical features.
The simplest solution we can think of to transform a categorical feature with k possible values is to map it to a numerical feature with values from 1 to k. For example, [Tech, Fashion, Fashion, Sports, Tech, Tech, Sports] becomes [1, 2, 2, 3, 1, 1, 3]. However, this will impose an ordinal characteristic, such as Sports is greater than Tech, and a distance property, such as Sports, is closer to Fashion than to Tech.
Instead, one-hot encoding converts the categorical feature to k binary features. Each binary feature indicates presence...
