CatBoost Object Importance implements another approach for importance calculations on a test sample prediction called FastLeafInfluence. Its foundation, LeafInfluence, approximates the concept of Leave-One-Out by changing the weights of the leaves where a training sample would end up instead of leaving it out and retraining. LeafInfluence calculates the change in loss on the prediction of a test sample dependent on the perturbation of the weights of the leaves associated with the training sample of interest. The FastLeafInfluence scores of HTS data can be interpreted analogously to KNN Shapley.