Pricing of Hanwoo beef in the Korean market is primarily based on meat quality, and particularly on marbling score. The ability to accurately predict marbling score early in the life of an animal is extremely valuable for producers to meet the requirements of their target market, and for genetic selection. A total of 3989 Korean Hanwoo cattle (2108 with 50 k SNP genotypes) and 45 phenotypic features were available for this study. Four machine learning (ML) algorithms were applied to predict six carcass traits and compared against linear regression prediction models. In most scenarios, SMO was the best performing algorithm. The most and least accurately predicted traits were carcass weight and marbling score with correlation of 0.95 and 0.64 respectively. Additionally, the value of using a synthetic minority over-sampling technique (SMOTE) was evaluated and results showed a slight improvement in the prediction error of marbling score. Machine Learning approaches can be useful tools to predict important carcass traits in beef cattle.

在韩国市场上，韩宇牛肉的定价主要基于肉的质量，特别是基于大理石花纹的评分。准确预测动物生命早期大理石花纹得分的能力对于生产者满足其目标市场的需求以及进行基因选择非常有价值。共有3989头韩国Hanwoo牛（2108个具有50 k SNP基因型）和45个表型特征可用于本研究。应用了四种机器学习（ML）算法来预测六个cas体特征，并与线性回归预测模型进行了比较。在大多数情况下，SMO是性能最好的算法。最准确和最不能准确预测的性状是car体重量和大理石花纹得分，相关性分别为0.95和0.64。此外，评估了使用合成少数族群过采样技术（SMOTE）的价值，结果显示，大理石花纹得分的预测误差略有改善。机器学习方法可以作为预测肉牛重要car体特征的有用工具。