Python Machine Learning Cookbook - Second Edition

One-hot encoding

We often deal with numerical values that are sparse and scattered all over the place. We don't really need to store these values. This is where one-hot encoding comes into the picture. We can think of one-hot encoding as a tool that tightens feature vectors. It looks at each feature and identifies the total number of distinct values. It uses a one-of-k scheme to encode values. Each feature in the feature vector is encoded based on this scheme. This helps us to be more efficient in terms of space.