What's the idea of Decision Tree Classifier?

The basic intuition behind a decision tree is to map out all possible decision paths in the form of a tree. It can be used for classification and regression (Decision Tree Regression ). In this post, let's try to understand the classifier.

Suppose that we have a dataset $S$ like in the figure below (ref)

An example of dataset $S$.

An example of dataset $S$.

A decision tree we want.

A decision tree we want.

There are many algorithms which can help us make a tree like above, in Machine Learning, we usually use:

Some basic concepts


ID3 algorithm

CART algorithm

Gini Impurity or Entropy?

Some points:(ref)