Using decision trees for discretization

Decision tree methods discretize continuous attributes during the learning process. A decision tree evaluates all possible values of a feature and selects the cut-point that maximizes the class separation, by utilizing a performance metric such as the entropy or Gini impurity. Then, it repeats the process for each node of the first data separation, along with each node of the subsequent data splits, until a certain stopping criterion has been reached. Therefore, by design, decision trees can find the set of cut-points that partition a variable into intervals with good class coherence.

Discretization with decision trees consists of using a decision tree to identify the optimal partitions for each continuous variable. In the Feature-engine implementation of this method, the decision tree is fit using the variable to discretize, and the target. After fitting, the decision tree is able to assign each observation to one of the N end leaves, generating...