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Kernel explainer for MLPRegressor

Given your familiarity with the admissions dataset, you will employ SHAP's Kernel Explainer to explain an MLPRegressor trained on this data. This method will allow you to critically assess how different features impact the model's predictions and verify these insights against your existing understanding of the dataset.

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

Bu egzersiz

Explainable AI in Python

kursunun bir parçasıdır
Kursu Görüntüle

Egzersiz talimatları

  • Create a SHAP Kernel Explainer using the MLPRegressor 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.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

import shap

# Create a SHAP Kernel Explainer
explainer = ____

# Calculate SHAP values
shap_values = ____

# Calculate mean absolute SHAP values
mean_abs_shap = ____

plt.bar(X.columns, mean_abs_shap)
plt.title('Mean Absolute SHAP Values for MLPRegressor')
plt.xticks(rotation=45)
plt.show()
Kodu Düzenle ve Çalıştır