Python Feature Engineering Cookbook

How it works...

In this recipe, we performed binary encoding using the Category Encoders package. We first loaded the dataset and divided it into train and test sets using train_test_split() from scikit-learn. Next, we used BinaryEncoder() to encode the A7 variable. With the fit() method, BinaryEncoder() created a mapping from category to set of binary columns, and with the transform() method, the encoder encoded the A7 variable in both the train and test sets.

With one-hot encoding, we would have created nine binary variables (k-1 = 10 unique categories - 1 = 9) to encode all of the information in A7. With binary encoding instead, we can represent the variable in less dimensions: log2(10)=3.3, that is, we need only four binary variables. ...