Evaluating K-Fold Cross Validation for Transformer Based Symbolic Regression Models
This incremental improvement could make symbolic regression more efficient in resource-constrained environments.
The paper tackles the problem of transformer-based symbolic regression models performing poorly on small datasets by applying k-fold cross-validation to a model trained on a reduced dataset of 15,000 points, resulting in a 53.31% relative improvement in validation loss.
Symbolic Regression remains an NP-Hard problem, with extensive research focusing on AI models for this task. Transformer models have shown promise in Symbolic Regression, but performance suffers with smaller datasets. We propose applying k-fold cross-validation to a transformer-based symbolic regression model trained on a significantly reduced dataset (15,000 data points, down from 500,000). This technique partitions the training data into multiple subsets (folds), iteratively training on some while validating on others. Our aim is to provide an estimate of model generalization and mitigate overfitting issues associated with smaller datasets. Results show that this process improves the model's output consistency and generalization by a relative improvement in validation loss of 53.31%. Potentially enabling more efficient and accessible symbolic regression in resource-constrained environments.