CART (Classification and Regression Tree) - SoojungHong/MachineLearning GitHub Wiki

1. Explanation of CART algorithm :
http://dni-institute.in/blogs/cart-algorithm-for-decision-tree/

2. Definition of CART :
Classification and Regression Tree (CART) is one of commonly used Decision Tree algorithms. In this post, we will explained the steps of CART algorithm using an example data.

Decision Tree is a recursive partitioning approach and CART split each of the input node into two child nodes, so CART decision tree is Binary Decision Tree. At each level of decision tree, the algorithm identify a condition – which variable and level to be used for splitting input node (data sample) into two child nodes.

3. CART Algorithm Steps :
Decision Tree building algorithm involves a few simple steps and these are:

Take Labelled Input data – with a Target Variable and a list of Independent Variables
Best Split: Find Best Split for each of the independent variables
Best Variable: Select the Best Variable for the split
Split the input data into Left and Right Nodes
Continue step 2-4 on each of the nodes until meet stopping criteria
Decision Tree Pruning : Steps to prune Decision Tree built

4. How to find the potential cut value :
List of potential cut values have to be identified first. One of the common approach is to find splits /cut off point is to take middle values. For this variable, the distinct values and then finding middle points.