利用随机森林预测比利时蓝牛杂交种的体重
Body weight prediction of Belgian Blue crossbred using random forest.
作者信息
Praharani Lisa, Talib Chalid, Kusumaningrum Diana Andrianita, Widiawati Yeni, Asmarasari Santiananda Arta, Rusdiana Supardi, Muttaqin Zultinur, Sianturi Ria Sari Gail, Wina Elizabeth, Sopian Endang, Arrazy Aqdi Faturahman, Adiati Umi, Saputra Ferdy
机构信息
Research Center for Animal Husbandry, Research Organization for Agriculture and Food, National Research Innovation Agency of the Republic of Indonesia, Bogor, Indonesia.
Indonesian Research Institute for Animal Production, Bogor, Indonesia.
出版信息
J Adv Vet Anim Res. 2024 Mar 31;11(1):181-184. doi: 10.5455/javar.2024.k763. eCollection 2024 Mar.
OBJECTIVE
The aim of this study was to predict the body weight (BW) of a Belgian Blue X Friesian Holstein (BB X FH) crossbred in Indonesia based on morphometrics using random forest.
MATERIALS AND METHODS
A total of 26 BB X FH crossbreds were observed for BW, chest weight (CW), body length (BL), hip height (HH), wither height (WH), and chest girth (CG) from 0, 30, 60, 90, 120, 150, 180, 210, 240, 270, and 300 days of age. Stepwise regression and random forest were performed using R 3.6.1.
RESULTS
The random forest results show that CG is an important variable in estimating BW, with an important variable value of 24.49%. Likewise, the results obtained by stepwise regression show that CG can be an indicator of selection for the BB X FH crossbred. The squared value obtained from the regression is 0.83, while the squared value obtained from the random forest (0.86) is greater than the regression.
CONCLUSION
In conclusion, random forest produces a better model than stepwise regression. However, a good simple equation to use to estimate BW is CG.
目的
本研究旨在基于形态测量学，使用随机森林预测印度尼西亚比利时蓝牛×弗里斯兰荷斯坦牛（BB×FH）杂交牛的体重（BW）。
材料与方法
观察了26头BB×FH杂交牛在0、30、60、90、120、150、180、210、240、270和300日龄时的体重、胸围（CW）、体长（BL）、体高（HH）、鬐甲高（WH）和胸围（CG）。使用R 3.6.1进行逐步回归和随机森林分析。
结果
随机森林分析结果表明，胸围是估计体重的重要变量，重要变量值为24.49%。同样，逐步回归结果表明，胸围可作为BB×FH杂交牛选择的指标。回归得到的决定系数为0.83，而随机森林得到的决定系数（0.86）大于回归分析的结果。
结论
总之，随机森林产生的模型比逐步回归更好。然而，用于估计体重的一个良好简单方程是胸围。
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