A SHAP (SHapley Additive exPlanations) summary plot is a type of visualization that shows the impact of different features on the output of a machine learning model. It is particularly useful for regression problems, where the goal is to predict a continuous output variable based on one or more input variables.
The basic idea behind a SHAP summary plot is to display the average impact of each input feature on the model's prediction, across all instances in the dataset. The plot shows a horizontal bar for each feature, with the length of the bar representing the magnitude of the feature's impact on the output. Features that have a positive impact are shown in blue, while those that have a negative impact are shown in red.
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X)
shap.summary_plot(shap_values, X)
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