Predicting body weight through biometric measurements in growing hair sheep using data mining and machine learning algorithms

Vázquez-Martínez, Ignacio; Tırınk, Cem; Salazar-Cuytun, Rosario; Mezo-Solis, Jesus A.; Garcia Herrera, Ricardo A.; Orzuna-Orzuna, José Felipe; Chay-Canul, Alfonso J.

doi:10.1007/s11250-023-03717-x

Cited by 1 publication

(1 citation statement)

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“…Predictive performance results of stepwise (SR) and ridge regression (RR) models and goodness-of-fit criteria of Corriedale ewes, are displayed in Table 5 and Figure (Huma and Iqbal, 2019;Çakmakçı, 2022;Tırınk et al, 2023;Vázquez-Martínez et al, 2023).…”

Section: Ridge Regressionmentioning

confidence: 99%

Predicting Body Weight from Body Measurements of Corriedale Sheep using Ridge and Stepwise Regression Models

Churata-Huacani,

William Canaza-Cayo,

Jesus Fernandes

et al. 2024

JAHP

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The objective of this research was to estimate body weight of Corriedale sheep from body measurements using ridge (RR) and stepwise regression (SR) models. On hundred (100) records of Corriedale ewes aged 1.5 and 2 years old were obtained in the Illpa Experimental Centre of the National University of Altiplano in Peru. Body weight (BW) and fourteen body measurements (BM) were taken including body length (BL), abdominal perimeter (AP), rump height (RH), wither height (WH), thoracic perimeter (TP), loin width (LWi), tail perimeter (TPe), hip width (HW), fore-shank width (FSW), tail width (TW), shoulder width (SW), forelimb length (FL), fore-shank length (FSL) and fore-shank perimeter (FSP). The variance inflation factor (VIF), mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R 2 ) were used to determine the best model for predicting body weight. The results revealed that both SR and RR models provided accurate fit to the data, however SR model showed slightly better than RR model to predict body weight of Corriedale ewes from AP, FSL, HW, LWi and BL measurements.

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Section: Ridge Regressionmentioning

confidence: 99%