You work for a retail company. You have created a Vertex AI forecast model that produces monthly item sales predictions. You want to quickly create a report that will help to explain how the model…

Creating a batch prediction job and comparing predictions to actual sales data only assesses the model's accuracy, not the underlying reasoning or feature contributions for individual predictions.

Configuring a batch prediction job to generate explanations on Vertex AI allows the model to produce feature attributions for each prediction, directly explaining which input features contributed most to the forecasted sales. This provides the necessary data for a report to quickly explain how the model calculates predictions for the actual sales data.

Generating counterfactual examples helps understand general model behavior under different inputs but is not the most direct or quickest method to generate a report specifically explaining the existing predictions for a given dataset.

Training another model is a time-consuming and resource-intensive process that does not directly provide explanations for the original model's predictions.