Relabel Minimal Training Subset to Flip a Prediction

Yang et al. (2023) discovered that removing a mere 1% of training points can often lead to the flipping of a prediction. Given the prevalence of noisy data in machine learning models, we pose the question: can we also result in the flipping of a test prediction by relabeling a small subset of the training data before the model is trained? In this paper, utilizing the extended influence function, we propose an efficient procedure for identifying and relabeling such a subset, demonstrating consistent success. This mechanism serves multiple purposes: (1) providing a complementary approach to challenge model predictions by recovering potentially mislabeled training points; (2) evaluating model resilience, as our research uncovers a significant relationship between the subset's size and the ratio of noisy data in the training set; and (3) offering insights into bias within the training set. To the best of our knowledge, this work represents the first investigation into the problem of identifying and relabeling the minimal training subset required to flip a given prediction.