Model Performance for Seedling Height Prediction in Rice Breeding

Rupam Basu published this dataset on 2026-03-17. It contains evaluation metrics for six statistical and machine learning models predicting seedling height (SDHT) in rice. The 5.5 KB XLS file compares RMSE, MAE, and predictive coverage from a five-fold cross-validation.

Use Cases

• Compare the predictive accuracy of Bayesian and frequentist models based on reported RMSE and MAE metrics.
• Assess model reliability for genomic selection in breeding programs based on predictive coverage statistics.
• Benchmark variable selection methods like LASSO and Spike-and-Slab for genetic data based on the described evaluation.

Strengths

• Dataset is openly licensed under CC-BY-4.0.
• Evaluation is based on a five-fold cross-validation, a standard method for assessing model performance.
• File size is 5.5 KB, making it quick to download and inspect.

Limitations

• Row count and specific column definitions are unknown, limiting suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.
• The dataset is very small (5.5 KB), indicating limited scope.

Provenance

Source

figshare

Collection Method

Likely contains results from a computational experiment comparing model performance.

Freshness

Last updated 2026-03-17 17:51:11; freshness should be verified.