Prediction of Body Weight by Using PCA-Supported Gradient Boosting and Random Forest Algorithms in Water Buffaloes ( Bubalus bubalis ) Reared in South-Eastern Mexico

• Published in: Animals (Basel) Vol. 14; no. 2; p. 293
• Main Authors: Gomez-Vazquez, Armando, Tırınk, Cem, Cruz-Tamayo, Alvar Alonzo, Cruz-Hernandez, Aldenamar, Camacho-Pérez, Enrique, Okuyucu, İbrahim Cihangir, Şahin, Hasan Alp, Dzib-Cauich, Dany Alejandro, Gülboy, Ömer, Garcia-Herrera, Ricardo Alfonso, Chay-Canul, Alfonso J
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01-01-2024
• Subjects: Accuracy; Algorithms; Body measurements; Body weight; buffalo; Comparative analysis; Datasets; Decision trees; Decision-making; Eigenvalues; Eigenvectors; gradient boosting; Machine learning; principal component analysis; Principal components analysis; Rain; random forest; Variables; Mexico; body weight; gradient boosting; random forest; principal component analysis; buffalo
• ISSN: 2076-2615; 2076-2615
• DOI: 10.3390/ani14020293

This study aims to use advanced machine learning techniques supported by Principal Component Analysis (PCA) to estimate body weight (BW) in buffalos raised in southeastern Mexico and compare their performance. The first stage of the current study consists of body measurements and the process of determining the most informative variables using PCA, a dimension reduction method. This process reduces the data size by eliminating the complex structure of the model and provides a faster and more effective learning process. As a second stage, two separate prediction models were developed with Gradient Boosting and Random Forest algorithms, using the principal components obtained from the data set reduced by PCA. The performances of both models were compared using R
, RMSE and MAE metrics, and showed that the Gradient Boosting model achieved a better prediction performance with a higher R
value and lower error rates than the Random Forest model. In conclusion, PCA-supported modeling applications can provide more reliable results, and the Gradient Boosting algorithm is superior to Random Forest in this context. The current study demonstrates the potential use of machine learning approaches in estimating body weight in water buffalos, and will support sustainable animal husbandry by contributing to decision making processes in the field of animal science.