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Decision Tree Implementation From Scratch

Aakash Pydi


Usage Instructions


The python script takes the following three arguments.

  1. trainingFile --> the path to the training set
  2. testFile --> the path to the test set
  3. model --> the model to use. Three options
  • vanilla --> for the full decision tree. For this model provide a fourth argument, a number indicating the training set percentage to use
  • depth --> for the decision tree with static depth. For this model also provide, a fourth argument indicating the training set percentage to use, a fifth argument indicating the validation set percentage to use and a sixth argument indicating the max-depth
  • prune --> for the decision tree with post-pruning. For this model, also provide, a fourth argument indicating the training set percentage to use, and a fifth argument indicating the validation set percentage to use.

Analysis


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.


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