Method and system to estimate model performance without ground truth based on model decision boundaries

Abstract

This disclosure relates to method and system to estimate model performance based on model decision boundaries. Absence of ground truths in a production scenario makes difficult to estimate the model behavior and performance. The disclosed method receives a set of unlabeled datapoints as input and a set of features are extracted from a training dataset associated with a model for which the performance is being estimated. Further, a nearest neighbor point of each unlabeled datapoint is computed from the training dataset with a first distance and a second distance and an associated ground truth. The performance of the model is estimated by evaluating a label assigned to each unlabeled datapoint from the set of unlabeled datapoints. Then, a decision boundary radius of the model is computed for the nearest neighbor point of each unlabeled datapoint based on the training dataset and the tolerance value using a Bayesian optimization technique.