Model Performance for Rice Grain Length Prediction

Rupam Basu's dataset compares the performance of six statistical models for predicting grain length (GRLT) in rice. The evaluation uses five-fold cross-validation and reports metrics including RMSE, MAE, and Predictive Coverage. The dataset was last updated on March 17, 2026, and is shared under a CC-BY-4.0 license.

Use Cases

• Compare the predictive accuracy of Bayesian and frequentist models based on reported RMSE and MAE metrics.
• Assess model reliability for genetic improvement based on the predictive coverage statistic.
• Benchmark variable selection methods like LASSO and Spike-and-Slab for genomic prediction in rice.

Strengths

• Provides a direct comparison of six distinct modeling approaches.
• Evaluation is based on a five-fold cross-validation methodology.
• Dataset is small (5.5 KB), facilitating quick download and inspection.

Limitations

• Row count and specific column-level documentation are absent.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Rupam Basu via figshare

Collection Method

Likely contains results from a computational experiment evaluating statistical models on rice genotype data.

Freshness

Last updated 2026-03-17 17:51:09; freshness should be verified.