SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation ... ... <看更多>

For example to explain an image, pixels can be grouped to superpixels and the prediction distributed among them. One innovation that SHAP brings to the ... ... <看更多>

I need to plot how each feature impacts the predicted probability for each sample from my LightGBM binary classifier. ... <看更多>

When SHAP does not use same assumption as LIME neighborhood, why does it require sample size to be mentioned? ... <看更多>