Decision Tree Example

A decision tree is a tree where each node represents a feature(attribute), each link(branch) represents a decision(rule) and each leaf represents an outcome(categorical or continues value).

This is a very simple implementation of it, in python, from scratch. Works for all discrete valued variables only.

For example, following is a decision tree to approve or reject loans:

This example code takes this following table, containing data about whether or not people play in a given weather condition(and we wish to make a decision tree on the same data):

Here is what the result looks like:

Future Plan

• Incorporate for continuous variables also, using Kmeans clustering
• Forest Implementation based on the same

Reference

https://medium.com/deep-math-machine-learning-ai/chapter-4-decision-trees-algorithms-b93975f7a1f1