In an embodiment, a computer hosts a machine learning (ML) model that infers a particular inference for a particular tuple that is based on many features. For each feature, and for each of many original tuples, the computer: a) randomly selects many perturbed values from original values of the feature in the original tuples, b) generates perturbed tuples that are based on the original tuple and a respective perturbed value, c) causes the ML model to infer a respective perturbed inference for each perturbed tuple, and d) measures a respective difference between each perturbed inference of the perturbed tuples and the particular inference. For each feature, a respective importance of the feature is calculated based on the differences measured for the feature. Feature importances may be used to rank features by influence and/or generate a local ML explainability (MLX) explanation.