# Decision Tree Implementation

I implemented three variants of a decision tree from scratch in this project. They are (a) a binary decision tree with no pruning using the ID3 algorithm, (b) a binary decision tree with a given maximum depth, and (c) a binary decision tree with post-pruning using reduced error pruning. (Link to Github Repo of Source Code). Link to this post on medium.

Here is some analysis I carried out to understand and compare the different implementations.

### Vanilla Model

A binary decision tree with no pruning using the ID3 algorithm.

### Depth Limited Model

A binary decision tree with a given maximum depth.

### Decision Tree with Post Pruning

A binary decision tree with post pruning.

Written on April 2, 2018