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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.21896 |
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| _version_ | 1866918075546206208 |
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| author | Kislay, Kaustubh Singh, Shlok Joshi, Soham Dutta, Rohan Shim, Jay Flint, George Zhu, Kevin |
| author_facet | Kislay, Kaustubh Singh, Shlok Joshi, Soham Dutta, Rohan Shim, Jay Flint, George Zhu, Kevin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_21896 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Evaluating K-Fold Cross Validation for Transformer Based Symbolic Regression Models Kislay, Kaustubh Singh, Shlok Joshi, Soham Dutta, Rohan Shim, Jay Flint, George Zhu, Kevin Machine Learning Computation and Language 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. |
| title | Evaluating K-Fold Cross Validation for Transformer Based Symbolic Regression Models |
| topic | Machine Learning Computation and Language |
| url | https://arxiv.org/abs/2410.21896 |