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