Neural Networks And Decision Trees

This blog explores a new approach to improving the explainability and transparency of neural networks. It shows that an equivalent decision tree can directly represent any neural network without altering the neural architecture, providing a better understanding of neural networks. Additionally, it allows for analyzing the categories a test sample belongs to, which can be extracted by the node rules that categorize the sample. This equivalence rationale of neural networks and decision trees has the potential to revolutionize the way we understand and interpret neural networks, making them more transparent and explainable.