Finding key medical charge predictors with SHAP
SHAP values provide insightful explanations for predictions made by machine learning models. Now, you'll utilize SHAP to decipher the influence of various features in a RandomForestRegressor model on predicting insurance charges.
X with the predictor features and y with the insurance charges, along with the RandomForest regressor model, have been pre-loaded for you.
Please note that the code might take some time to run.
This exercise is part of the course
Explainable AI in Python
Exercise instructions
- Initialize a SHAP tree explainer named
explainerfor the RandomForestmodel. - Calculate
shap_valuesfor the dataset. - Compute the mean absolute SHAP values to identify the most influential features.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
import shap
# Create a SHAP Tree 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 RandomForest')
plt.xticks(rotation=45)
plt.show()