When we transform features, our aim, obviously, is to make them more useful to our models. This can be done by adding, removing, or changing information represented by the feature. A common feature transformation is that of changing the feature type. A typical example is binarization, that is, transforming a categorical feature into a set of binary ones. Another example is changing an ordinal feature into a categorical feature. In both these cases, we lose information. In the first instance, the value of a single categorical feature is mutually exclusive, and this is not conveyed by the binary representation. In the second instance, we lose the ordering information. These types of transformations can be considered inductive because they consist of a well-defined logical procedure that does not involve an objective choice apart from the decision to carry out these transformations in the first place.
Binarization can be easily carried out using the sklearn.preprocessing...
