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How can I improve the interpretability of a neural network model?

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How can I improve the interpretability of a neural network model?

Asked on Jan 23, 2026

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Improving the interpretability of a neural network model involves using techniques that help explain how the model makes decisions. This can be achieved through visualization, simplification, and explanation methods.

Example Concept: One common approach to enhance interpretability is using Layer-wise Relevance Propagation (LRP), which assigns relevance scores to each input feature based on its contribution to the final prediction. This helps in understanding which parts of the input are most influential in the model's decision-making process.

Additional Comment:

Visualization tools like saliency maps or Grad-CAM can highlight important areas in input data, such as images.
Simplifying models by using fewer layers or neurons can make them easier to interpret, though this might affect performance.
Using surrogate models, like decision trees, to approximate neural network behavior can provide insights into decision paths.
Feature importance techniques can help identify which input features are most impactful in the model's predictions.

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How can I improve the interpretability of a neural network model?

Asked on Jan 23, 2026

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