SHAP for explaining income levels
Practice using SHAP to analyze and visualize how each feature influences a trained model's predictions on a single sample from the income dataset, using a waterfall plot for deeper insight into feature contributions.
A trained KNN model is loaded for you. The dataset containing features is loaded in X.
This exercise is part of the course
Explainable AI in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
import shap
# Create the SHAP explainer
explainer = ____
# Compute SHAP values for the first instance in X
shap_values = ____
print(shap_values)