Understanding overfitting in random forest for probability estimation: a visualization and simulation study
Abstract Background Random forests have become popular for clinical risk prediction modeling. In a case study on predicting ovarian malignancy, we observed training AUCs close to 1. Although this suggests overfitting, performance was competitive on test data. We aimed to understand the behavior of r...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2024-09-01
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| Series: | Diagnostic and Prognostic Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s41512-024-00177-1 |
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