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Simplified implementation of “Neural Networks are Decision Trees”.
Simplified implementation of “Neural Networks are Decision Trees”.
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Showing that any neural network with any activation function can be represented as a decision tree. Since decision trees are inherently interpretable, their equivalence helps us understand how the network makes decisions.
Showing that any neural network with any activation function can be represented as a decision tree. Since decision trees are inherently interpretable, their equivalence helps us understand how the network makes decisions.
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In this implementation, we trained a simple neural network for 1k epochs on makemoons, saved the trained weights (state dicts), extracted the decision tree equivalent from the trained weight then visualize and evaluate.
In this implementation, we trained a simple neural network for 1k epochs on makemoons, saved the trained weights (state dicts), extracted the decision tree equivalent from the trained weight then visualize and evaluate.
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Code: 
Code: 
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https://github.com/Jaykef/ai-algorithms/blob/main/nns_are%20decision_trees.ipynb
https://github.com/Jaykef/ai-algorithms/blob/main/nns_are%20decision_trees.ipynb
Simplified implementation of “Neural Networks are Decision Trees”.

Showing that any neural network with any activation function can be represented as a decision tree. Since decision trees are inherently interpretable, their equivalence helps us understand how the network makes decisions.

In this implementation, we trained a simple neural network for 1k epochs on makemoons, saved the trained weights (state dicts), extracted the decision tree equivalent from the trained weight then visualize and evaluate.

Code: https://github.com/Jaykef/ai-algorithms/blob/main/nns_are%20decision_trees.ipynb
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