Usage of the XGBoost and MARS algorithms for predicting body weight in Kajli sheep breed

Faraz A., Tırınk C., Önder H., Şen U., Ishaq H. M., Tauqir N. A., ...Daha Fazla

TROPICAL ANIMAL HEALTH AND PRODUCTION, cilt.55, sa.4, 2023 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 55 Sayı: 4
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1007/s11250-023-03700-6
  • Dergi Adı: TROPICAL ANIMAL HEALTH AND PRODUCTION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Agricultural & Environmental Science Database, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, Environment Index, Veterinary Science Database
  • Anahtar Kelimeler: Body weight, Kajli sheep, MARS, Sheep, XGBoost
  • Ondokuz Mayıs Üniversitesi Adresli: Evet

Özet

This study aimed to utilize the XGBoost and MARS algorithms to predict present weight from body measurements. The algorithms have the potential to model nonlinear relationships between body measurements and weight, and this study attempted to find a model that provided the most accurate predictions of present weight. The current study was conducted with 152 animals in order to achieve a certain goal. To compare the model performances, goodness-of-fit criteria such as R-2, r, RMSE, CV, SDratio, PI, MAPE, AIC were used. According to the results of this study, the XGBoost algorithm was the most reliable model for predicting present weight from body measurement. Even if the XGBoost algorithm was the most accurate model, the MARS algorithm was the reliable model for the same aim. In addition, it is hoped that the results of this study will help researchers and breeders better understand the relationship between body measurements and weight and ultimately be able to help individuals better manage their weight. As a conclusion, in the current study, the XGBoost algorithm is an effective, efficient, and reliable tool for accurately estimating present weight from body measurements. This makes it an invaluable tool in rural areas, where traditional weighing scales may not be available or reliable.