Decision tree classifiers produce rules in simple English sentences, which can easily be interpreted and presented to senior management without any editing. Decision trees can be applied to either classification or regression problems. Based on features in data, decision tree models learn a series of questions to infer the class labels of samples.
In the following figure, simple recursive decision rules have been asked by a programmer himself to do relevant actions. The actions would be based on the provided answers for each question, whether yes or no.
Decision Trees do not have much machinery as compared with logistic regression. Here we have a few metrics to study. We will majorly focus on impurity measures; decision trees split variables recursively based on set impurity criteria until they reach some stopping criteria (minimum observations per terminal node, minimum observations for split at any node, and so on):
- Entropy...
