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How can I improve the interpretability of a deep learning model?

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How can I improve the interpretability of a deep learning model?

Asked on Feb 07, 2026

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Improving the interpretability of a deep learning model involves using techniques that make the model's predictions more understandable to humans. This can be achieved through various methods such as visualization, feature importance, and surrogate models.

Example Concept: One common approach to enhance interpretability is using feature importance techniques like SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations). These methods provide insights into which features contribute most to the model's predictions by approximating the model locally around the prediction of interest. This helps in understanding the model's decision-making process by highlighting the impact of each feature.

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Visualization techniques such as saliency maps or Grad-CAM can be used for models dealing with image data to show which parts of an image are influencing the decision.
Surrogate models like decision trees can be trained on the predictions of the deep learning model to provide a more interpretable approximation.
Regularization techniques can also be employed to simplify the model, potentially making it more interpretable.
It's important to balance interpretability with model accuracy, as increasing one can sometimes decrease the other.

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How can I improve the interpretability of a deep learning model?

Asked on Feb 07, 2026

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