Multi-metric evaluation and parametric optimization of stochastic gradient boosting machines for genomic prediction and selection in wheat (Triticum aestivum) breeding.

G3 (Bethesda) · 2026 · PMC13334169 · PMID 42175721

Europe PMC DOI

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0.1520

closest p · 3.0× alpha

0.1520

boldest claim

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highly significantp = 0.152not close (p > 0.1)

In TGW, both hyperparameters showed large, highly significant effects for learning rate ( F = 61.39, η 2 p = 0.152, P < 0.001) and boosting rounds ( F = 98.87, η 2 p = 0.224, P < 0.001), and the learning rate × boosting rounds interaction also contributed a low but significant effect ( F = 2.98, η 2 p = 0.042, P < 0.001).

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Multi-metric evaluation and parametric optimization of stochastic gradient boosting machines for genomic prediction and selection in wheat (Triticum aestivum) breeding.

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Multi-metric evaluation and parametric optimization of stochastic gradient boosting machines for genomic prediction and selection in wheat (Triticum aestivum) breeding.