Intelligence Random Forest Application in Developing Regression Model from Lamb Carcass C-Site Fat Depth Data
Sin Jie, Elayaraja Aruchunan, Nur Anisah Mohamed A. Rahman, Majid Khan Majahar Ali, Suhana Mohezar Ali, Muhammad Ashraf Khalid, Jayaseelan Marimuthu, Graham Edwin Gardner, Samsul Ariffin Abdul Karim · 2024
This study aims to optimize predictive modeling for lamb carcass C-site fat depth using machine learning algorithms, addressing limitations of traditional measurement methods. Employing random forest regression and multiple linear regression, the research identifies multiple linear regression with K-means clustering as the top performer in accuracy metrics (MSE, RMSE, https://www.w3.org/1998/Math/MathML" display="inline"> R https://www.w3.org/1999/xlink" xlink:href=" https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9781003400387/2749f5d6-f762-4fa3-9f51-835205c7b9b9/content/math9_1.tif "/> -squared, adjusted https://www.w3.org/1998/Math/MathML" display="inline"> R https://www.w3.org/1999/xlink" xlink:href=" https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9781003400387/2749f5d6-f762-4fa3-9f51-835205c7b9b9/content/math9_2.tif "/> -squared, MAE). The results show that the random forest regression with K -means clustering emerges as the preferred model for fat depth prediction. The study contributes insights into accurate fat depth estimation and highlights the potential of machine learning in enhancing accuracy beyond conventional methods.