Train regional surrogate models to explain individual predictions, focusing on geographical and usage patterns.

Calculate the effect of each feature as the weight of the feature multiplied by the feature value, providing a linear approximation of feature importance.

Set up integrated gradients explanations on Vertex Explainable AI to understand the contribution of each feature to the prediction by integrating the gradients along the path from a baseline to the input.

Set up sampled Shapley explanations on Vertex Explainable AI to fairly distribute the contribution of each feature to the prediction, considering all possible combinations of features.

Combine both integrated gradients and sampled Shapley explanations to leverage the strengths of both methods for a comprehensive understanding of the prediction.