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Exercice

Kernel explainer for MLPClassifier

Neural networks can be very accurate, but understanding their decisions can be challenging due to complexity. Now, you'll leverage the SHAP Kernel Explainer to interpret an MLPClassifier trained on the adult income dataset. You will explore which of the three features—age, education, or hours worked per week—is most important for predicting income according to this model.

X containing the predictors and y containing the admission decisions, along with the pre-trained MLPClassifier model, have been pre-loaded for you.

Instructions

100 XP
  • Instantiate a SHAP Kernel Explainer using the MLPClassifier model and a k-means summary of 10 samples from X.
  • Generate shap_values for X.
  • Compute the mean absolute SHAP values to identify key factors affecting admissions.