## 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* $S$*.*

*A decision tree we want.*

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

**ID3** (*Iterative Dichotomiser*): uses **information gain** / **entropy**.
**CART** (*Classification And Regression Tree*): uses **Gini impurity**.

### Some basic concepts

**Splitting**: It is a process of dividing a node into two or more sub-nodes.
**Pruning**: When we remove sub-nodes of a decision node, this process is called pruning.
**Parent node and Child Node**: A node, which is divided into sub-nodes is called parent node of sub-nodes where as sub-nodes are the child of parent node.

### ID3 algorithm

- ID3 algorithm (TL;DR;)
- ID3 algorithm in detail

### CART algorithm

- CART algorithm (TL;DR;)
- CART algorithm in detail

### Gini Impurity or Entropy?

Some points:(ref)