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AI can make good predictions but since the process between input and output is opaque, the logic behind those predictions is hard to understand. And Explainable AI can be a solution for such problem. Here we use SHAP to help use understanding why the model makes certain decisions. This tool utilizes SHAP values that are based on Shapley values.
In this experiment, we will present three visualization methods provided by the Python SHAP package. They are bar plot, beeswarm plot, and force plot.
Bar plot simply takes the mean absolute value of the SHAP values per feature across the data. It shows the SHAP feature importance in descending order.
Beeswarm plot is also known as summary plot. It gives an overview of which features are more important for a model by plotting the SHAP values of every feature for every instance. The position on the x-axis represents the SHAP value of an instance and the colour represents the value of the feature. For each feature, instances with the same SHAP value are placed along the y-axis, this gives us a sense of distribution of SHAP value. Force plot visualizes every feature as force that either increases or decreases the prediction by pushing it from the baseline for Shapley value to the model output.
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IEEE/ICACT20220024 Slide.06
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