ai-final-project's People
ai-final-project's Issues
Call the OCaml decision tree program from Python
Most of the decision tree cross-validation code is present using a mocked version of the classifier. Once we have the OCaml binaries done (or close to done) we should replace the mocked versions with the final binary calls.
Implement decision tree pruning
In order to avoid over-fitting, we should implement ฯ2 pruning as described in 18.3.5 of the textbook.
Write data parser for decision tree learning
Test decision tree learning
I added a few functions for decision tree learning, but we should test them more completely. I currently just have a basic coin-flip test example that returns the expected 0 information gain, but should add in some more canonical examples.
Select ML repository examples
We should select 4 examples from the ML Repository and include a description of the chosen datasets in the readme.
Design JSON schema for decision trees
Additionally, take a crack at making some sort of (de)serializer for the JSON schema so we don't have to explicitly care about the specification.
Write a classifier function
The function should take two file names as inputs: a file containing a decision tree and another for a file containing the corresponding dataset.
Design internal representation of decision trees
Add "possible classification" argument to decision tree generator
Write a paragraph describing what each team member did
@willem-dejong @Diego-Castaneda Comment below with a paragraph describing what you did on the project.
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