The Machine Learning “Advent Calendar” Day 7: Decision Tree Classifier

A Decision Tree Classifier determines its optimal split by minimizing an impurity measure such as Gini impurity or Entropy. These measures quantify the homogeneity of a node, reaching zero for pure nodes (one class) and a maximum for evenly mixed classes. The algorithm evaluates all potential splits, calculates the weighted impurity for the resulting regions, and selects the split that yields the lowest overall impurity. This same principle applies to both binary and multi-class classification problems, with the impurity formula simply extending to more classes. While Gini and Entropy are calculated differently, they are functionally similar and usually lead to identical splits in practice.