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Genomic prediction
Rice Flowering Time Genomic Prediction
This rice panel contains SNP genotype profiles and flowering-time measurements, with one row per plant line. The regression task predicts flowering time from genetic markers using the data assembled for Azodi and colleagues' genomic-prediction comparison.
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At this operating point
Model comparison
p=10,000, n=100 · Lower RMSE is better
No scores at this operating point. Select another feature or sample budget.
Random Forest is marked with a diamond. Models are ordered by the selected metric.
Across sample budgets
Learning curves
All metric values at this operating point
| Model | RMSE | MAE | R² |
|---|---|---|---|
| Random Forest | 0.9204 | 0.702 | 0.1359 |
| Extra Trees | 0.9244 | 0.706 | 0.129 |
| Random Forest (tuned) | 0.9267 | 0.7034 | 0.125 |
| Extra Trees (tuned) | 0.928 | 0.7092 | 0.124 |
| Kumo Tabular (M) | 0.9281 | 0.699 | 0.1225 |
| TabPFN 3.5 | 0.9359 | 0.7161 | 0.1088 |
| RealTabPFN 2.5 | 0.9419 | 0.7279 | 0.09621 |
| TabDPT | 0.9464 | 0.7371 | 0.09005 |
| RealTabPFN v2 | 0.9524 | 0.7387 | 0.07601 |
| AutoGluon | 0.9579 | 0.7405 | 0.0677 |
| XGBoost (tuned) | 0.9602 | 0.7438 | 0.05886 |
| TabM | 0.9688 | 0.7553 | 0.04224 |
| XGBoost | 0.9718 | 0.7579 | 0.03515 |
| MLP (tuned) | 0.9742 | 0.7576 | 0.03151 |
| GeneICL | 0.9749 | 0.7508 | 0.03364 |
| MLP | 0.9887 | 0.7694 | 0.004667 |
| MITRA | 0.9892 | 0.7775 | 0.0003888 |
| Causilo | 0.991 | 0.7531 | -0.003309 |
| Logistic Regression (tuned) | 0.9981 | 0.7673 | -0.01789 |
| LimiX2 | 0.9984 | 0.7579 | -0.02875 |
| TabFM | 1.003 | 0.7686 | -0.02137 |
| Logistic Regression | 1.017 | 0.7824 | -0.05896 |
| RealMLP | 1.054 | 0.847 | -0.1471 |