Generalizability through Explainability: Countering Overfitting with Counterfactual Examples

• URL: http://arxiv.org/abs/2502.09193v1
• Date: Thu, 13 Feb 2025 11:33:17 GMT
• Title: Generalizability through Explainability: Countering Overfitting with Counterfactual Examples
• Authors: Flavio Giorgi, Fabiano Veglianti, Fabrizio Silvestri, Gabriele Tolomei,
• Abstract summary: Overfitting is a well-known issue in machine learning that occurs when a model struggles to generalize its predictions to new, unseen data. Traditional techniques to mitigate overfitting include early stopping, data augmentation, and regularization. We introduce CF-Reg, a novel regularization term in the training loss that controls overfitting by ensuring enough margin between each instance and its corresponding counterfactual.
• Score: 6.3107782051840555
• License:
• Abstract: Overfitting is a well-known issue in machine learning that occurs when a model struggles to generalize its predictions to new, unseen data beyond the scope of its training set. Traditional techniques to mitigate overfitting include early stopping, data augmentation, and regularization. In this work, we demonstrate that the degree of overfitting of a trained model is correlated with the ability to generate counterfactual examples. The higher the overfitting, the easier it will be to find a valid counterfactual example for a randomly chosen input data point. Therefore, we introduce CF-Reg, a novel regularization term in the training loss that controls overfitting by ensuring enough margin between each instance and its corresponding counterfactual. Experiments conducted across multiple datasets and models show that our counterfactual regularizer generally outperforms existing regularization techniques.

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