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Information Bottleneck (IB) for decision trees, with many visualizations about decision trees and the impact of data shape, noise and regularization.

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Information Bottleneck as Regularizer for Decision Trees

This repository is a proof-of-concept implementation of decision trees trained with the loss function proposed in The Information Bottleneck, and a presentation about it. The idea andthe usage of the code are also explained in the notebook.

This is my project for the the lecture Information Theory of Prof. Caire at TU Berlin, so thank you for the patience!

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Clone the repository and run: pip install .

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Information Bottleneck (IB) for decision trees, with many visualizations about decision trees and the impact of data shape, noise and regularization.

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