Feature importance is a technique that calculates a score for all input features for a given model. The score simply represents the "importance" of each feature. The higher the score, the more influence a specific element has on the model. When training the downstream predictive model using original data and synthetic data, the feature importance of both should be comparable. Feature importance shows the usefulness of synthetic data.