Goal¶

This post aims to introduce how to explain Image Classification (trained by PyTorch) via SHAP Deep Explainer.

Shap is the module to make the black box model interpretable. For example, image classification tasks can be explained by the scores on each pixel on a predicted image, which indicates how much it contributes to the probability positively or negatively.

Reference

• Github for shap - PyTorch Deep Explainer MNIST example.ipynb
• Step-by-step Data Science - Interpretability of prediction for Boston Housing using SHAP

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