A Factorial Investigation of Hyperparameter Tuning Strategies for Lasso- Based Genomic Prediction

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Year : 2026 | Volume : 15 | 02 | Page :
By

Vaibhav Godase,

  1. Assistant professor, Department of Electronics & Telecommunication Engineering, SKN Sinhgad College of Engineering, Pandharpur, India, Maharashtra, India

Abstract

In an earlier comparative study of machine-learning methods for genomic prediction of wheat grain yield, we reported a counter-intuitive result: automated nested-cross-validation tuning of the Lasso regularization penalty reduced mean predictive ability relative to a fixed, arbitrarily chosen penalty (mean Pearson r falling from 0.408 to 0.349 across four environments), the opposite of the expected effect of hyperparameter tuning. We hypothesized two possible explanations at the time — high-variance penalty selection from a small number of inner cross- validation folds, and a mismatch between the inner tuning objective (mean squared error) and the outer evaluation metric (Pearson correlation) — but did not test them. Here we do so directly, with a controlled 2×2 factorial experiment (inner-fold count: 3 vs. 10; inner scoring objective: mean squared error vs. Pearson correlation) using a single, fixed penalty grid and a single, fixed outer cross-validation split identical to our companion studies, isolating each factor’s individual contribution. Both factors improved mean predictive ability: increasing inner folds from 3 to 10 2 improved r by 0.017 (under MSE scoring) or 0.006 (under correlation scoring), and switching from MSE to correlation scoring improved r by 0.013 (at 3 inner folds) or 0.002 (at 10 inner folds). The two factors were therefore each real but sub-additive — each closes part of the same gap, so combining them yields less improvement than their individually measured effects would suggest if added naively. Even our best controlled combination (10 inner folds, correlation scoring; mean r = 0.405) did not fully recover the fixed-penalty baseline (0.408), and substantially exceeded our original coarse LassoCV result (0.349), pointing to a third, previously unidentified contributing factor: scikit-learn’s LassoCV selects its own data-driven alpha search range automatically, and this automatic range, not just fold count or scoring objective, appears to have been part of the original problem. We report all three findings, including the fact that our controlled experiment does not fully explain the original anomaly, as an honest account of a debugging process rather than a fully closed case.

Keywords: genomic selection; Lasso regression; hyper parameter tuning; nested cross- validation; factorial experiment; wheat; computational genomics

How to cite this article: Vaibhav Godase. A Factorial Investigation of Hyperparameter Tuning Strategies for Lasso- Based Genomic Prediction. Research and Reviews : Journal of Computational Biology. 2026; 15(02):-.
How to cite this URL: Vaibhav Godase. A Factorial Investigation of Hyperparameter Tuning Strategies for Lasso- Based Genomic Prediction. Research and Reviews : Journal of Computational Biology. 2026; 15(02):-. Available from: https://journals.stmjournals.com/rrjocb/article=2026/view=251945

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Ahead of Print Subscription Original Research
Volume 15
02
Received 13/07/2026
Accepted 17/07/2026
Published 27/07/2026
Publication Time 14 Days

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