Interpreting regressors locally

You are provided with a KNN regressor model that predicts health insurance costs based on features such as age, sex, BMI, number of children, and smoking status. Your task is to assess how each feature affects the prediction for a given sample.

The KNN model and the necessary packages are pre-loaded for you.

This exercise is part of the course

Explainable AI in Python

View Course

Exercise instructions

Create a LIME explainer for the KNN regressor model.
Generate an explanation for the model's prediction on the provided sample_data_point.
Display the influence of each feature on the prediction.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

from lime.lime_tabular import LimeTabularExplainer

sample_data_point = X.iloc[2, :]

# Create the explainer
explainer = ____

# Generate the explanation
exp = ____

# Display the explanation
exp.____
plt.show()

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