August 15, 2025Scientia AgropecuariaOpen Access

Balancing accuracy, interpretability, and stability in machine-learning models: Live-weight prediction of Andean sheep from morphometric traits

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Comparative analysis shows predictive capability of morphometric traits in Corriedale lambs, suggesting stability in machine learning models.

Models demonstrated excellent predictive capability with a coefficient of determination R2 = 0.89, showing strong predictive accuracy for live weight.
Multiple linear regression and Ridge regression achieved the lowest mean squared error of MSE = 0.083, indicating their high stability in predictions.
Assessment included various algorithms such as decision trees and XGBoost to evaluate their effectiveness in live weight prediction.
Findings highlight the importance of using balanced models for precise feed allocation and monitoring growth in sheep production.

Carhuas et al. (2025) studied this question.

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August 15, 2025Scientia AgropecuariaOpen Access

View Full Paper

Ask AI

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Authors

Comparative analysis shows predictive capability of morphometric traits in Corriedale lambs, suggesting stability in machine learning models.

Models demonstrated excellent predictive capability with a coefficient of determination R2 = 0.89, showing strong predictive accuracy for live weight.
Multiple linear regression and Ridge regression achieved the lowest mean squared error of MSE = 0.083, indicating their high stability in predictions.
Assessment included various algorithms such as decision trees and XGBoost to evaluate their effectiveness in live weight prediction.
Findings highlight the importance of using balanced models for precise feed allocation and monitoring growth in sheep production.

Carhuas et al. (2025) studied this question.

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