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.

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Depth Limited Model

A binary decision tree with a given maximum depth.

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Decision Tree with Post Pruning

A binary decision tree with post pruning.

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