Shap Explainer


This code calculates and visualizes the feature importance of a machine learning model using SHAP values.

First, a SHAP explainer object is created with the shap.Explainer() function, which takes the trained model as an argument.

Next, SHAP values are computed for the test data using the explainer object, which returns a matrix of SHAP values for each instance and feature.

Finally, a summary plot of the SHAP values is created using the shap.summary_plot() function, which shows the features sorted by importance and their corresponding SHAP values. The plot_type argument is set to "bar" to create a bar chart of the feature importance.

 1|  import shap
 3|  explainer = shap.Explainer(model)
 4|  shap_values = explainer(X_test)
 5|  shap.summary_plot(shap_values, X_test, plot_type="bar")
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