### SHAP Values
SHAP (SHapley Additive exPlanations) values are a method for explaining the output of a machine learning model by assigning a value to each feature for a specific prediction. It's a powerful tool for understanding how individual features contribute to the model's predictions.
**Example:** Suppose we have a model that predicts house prices based on features like number of bedrooms, square footage, and location. We want to calculate the SHAP values for a specific prediction.
**Constraints:** Use the SHAP library to calculate SHAP values.
Test Cases
Test Case 1
Input:
[[1, 2, 3], [4, 5, 6]]Expected:
[[0.1, 0.2, 0.7], [0.3, 0.4, 0.3]]Test Case 2
Input:
[[7, 8, 9], [10, 11, 12]]Expected:
[[0.5, 0.2, 0.3], [0.1, 0.7, 0.2]]+ 3 hidden test cases