{"title":"比较机器学习算法和多元线性回归法对阿卡拉曼羔羊活重的估算。","authors":"Özge Kozaklı, Ayhan Ceyhan, Mevlüt Noyan","doi":"10.1007/s11250-024-04049-0","DOIUrl":null,"url":null,"abstract":"<p><p>This study was designed to predict the post-weaning weights of Akkaraman lambs reared on different farms using multiple linear regression and machine learning algorithms. The effect of factors the age of the dam, gender, type of lambing, enterprise, type of flock, birth weight, and weaning weight was analyzed. The data was collected from a total of 25,316 Akkaraman lambs raised at multiple farms in the Çiftlik District of Niğde province. Comparative analysis was conducted by using multiple linear regression, Random Forest, Support Vector Machines (and Support Vector Regression), Extreme Gradient Boosting (XGBoost) (and Gradient Boosting), Bayesian Regularized Neural Network, Radial Basis Function Neural Network, Classification and Regression Trees, Exhaustive Chi-squared Automatic Interaction Detection (and Chi-squared Automatic Interaction Detection), and Multivariate Adaptive Regression Splines algorithms. In this study, the test dataset was divided into five layers using the K-fold cross-validation method. The performance of models was compared using performance criteria such as Adjusted R-squared (Adj-[Formula: see text]), Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD), and Mean Absolute Percentage Error (MAPE) by utilizing test populations in the predicted models. Additionally, the presence of low standard deviations for these criteria indicates the absence of an overfitting problem. [Formula: see text]The comparison results showed the Random Forest algorithm had the best predictive performance compared to other algorithms with Adj-[Formula: see text], RMSE, MAD, and MAPE values of 0.75, 3.683, 2.876, and 10.112, respectively. In conclusion, the results obtained through Multiple Linear Regression for the live weights of Akkaraman lambs were less accurate than the results obtained through artificial neural network analysis.</p>","PeriodicalId":23329,"journal":{"name":"Tropical animal health and production","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11371844/pdf/","citationCount":"0","resultStr":"{\"title\":\"Comparison of machine learning algorithms and multiple linear regression for live weight estimation of Akkaraman lambs.\",\"authors\":\"Özge Kozaklı, Ayhan Ceyhan, Mevlüt Noyan\",\"doi\":\"10.1007/s11250-024-04049-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>This study was designed to predict the post-weaning weights of Akkaraman lambs reared on different farms using multiple linear regression and machine learning algorithms. The effect of factors the age of the dam, gender, type of lambing, enterprise, type of flock, birth weight, and weaning weight was analyzed. The data was collected from a total of 25,316 Akkaraman lambs raised at multiple farms in the Çiftlik District of Niğde province. Comparative analysis was conducted by using multiple linear regression, Random Forest, Support Vector Machines (and Support Vector Regression), Extreme Gradient Boosting (XGBoost) (and Gradient Boosting), Bayesian Regularized Neural Network, Radial Basis Function Neural Network, Classification and Regression Trees, Exhaustive Chi-squared Automatic Interaction Detection (and Chi-squared Automatic Interaction Detection), and Multivariate Adaptive Regression Splines algorithms. In this study, the test dataset was divided into five layers using the K-fold cross-validation method. The performance of models was compared using performance criteria such as Adjusted R-squared (Adj-[Formula: see text]), Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD), and Mean Absolute Percentage Error (MAPE) by utilizing test populations in the predicted models. Additionally, the presence of low standard deviations for these criteria indicates the absence of an overfitting problem. [Formula: see text]The comparison results showed the Random Forest algorithm had the best predictive performance compared to other algorithms with Adj-[Formula: see text], RMSE, MAD, and MAPE values of 0.75, 3.683, 2.876, and 10.112, respectively. In conclusion, the results obtained through Multiple Linear Regression for the live weights of Akkaraman lambs were less accurate than the results obtained through artificial neural network analysis.</p>\",\"PeriodicalId\":23329,\"journal\":{\"name\":\"Tropical animal health and production\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2024-09-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11371844/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Tropical animal health and production\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://doi.org/10.1007/s11250-024-04049-0\",\"RegionNum\":3,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"AGRICULTURE, DAIRY & ANIMAL SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Tropical animal health and production","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1007/s11250-024-04049-0","RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AGRICULTURE, DAIRY & ANIMAL SCIENCE","Score":null,"Total":0}
引用次数: 0
摘要
本研究旨在利用多元线性回归和机器学习算法预测不同农场饲养的阿卡拉曼羔羊的断奶后体重。研究分析了母羊年龄、性别、产羔类型、企业、羊群类型、出生体重和断奶体重等因素的影响。数据收集自尼德省 Çiftlik 地区多个农场饲养的 25,316 只阿卡拉曼羔羊。比较分析采用了多元线性回归、随机森林、支持向量机(和支持向量回归)、极梯度提升(XGBoost)(和梯度提升)、贝叶斯正则化神经网络、径向基函数神经网络、分类树和回归树、详尽卡方自动交互检测(和卡方自动交互检测)以及多元自适应回归样条算法。本研究采用 K 折交叉验证法将测试数据集分为五层。通过在预测模型中使用测试种群,使用调整 R 平方(Adj-[公式:见正文])、均方根误差(RMSE)、平均绝对偏差(MAD)和平均绝对百分比误差(MAPE)等性能标准对模型的性能进行比较。此外,如果这些标准的标准偏差较低,则表明不存在过拟合问题。[计算公式:见正文]比较结果表明,与其他算法相比,随机森林算法的预测性能最佳,其 Adj-[计算公式:见正文]、RMSE、MAD 和 MAPE 值分别为 0.75、3.683、2.876 和 10.112。总之,通过多元线性回归得出的阿卡拉曼羔羊活重结果不如通过人工神经网络分析得出的结果准确。
Comparison of machine learning algorithms and multiple linear regression for live weight estimation of Akkaraman lambs.
This study was designed to predict the post-weaning weights of Akkaraman lambs reared on different farms using multiple linear regression and machine learning algorithms. The effect of factors the age of the dam, gender, type of lambing, enterprise, type of flock, birth weight, and weaning weight was analyzed. The data was collected from a total of 25,316 Akkaraman lambs raised at multiple farms in the Çiftlik District of Niğde province. Comparative analysis was conducted by using multiple linear regression, Random Forest, Support Vector Machines (and Support Vector Regression), Extreme Gradient Boosting (XGBoost) (and Gradient Boosting), Bayesian Regularized Neural Network, Radial Basis Function Neural Network, Classification and Regression Trees, Exhaustive Chi-squared Automatic Interaction Detection (and Chi-squared Automatic Interaction Detection), and Multivariate Adaptive Regression Splines algorithms. In this study, the test dataset was divided into five layers using the K-fold cross-validation method. The performance of models was compared using performance criteria such as Adjusted R-squared (Adj-[Formula: see text]), Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD), and Mean Absolute Percentage Error (MAPE) by utilizing test populations in the predicted models. Additionally, the presence of low standard deviations for these criteria indicates the absence of an overfitting problem. [Formula: see text]The comparison results showed the Random Forest algorithm had the best predictive performance compared to other algorithms with Adj-[Formula: see text], RMSE, MAD, and MAPE values of 0.75, 3.683, 2.876, and 10.112, respectively. In conclusion, the results obtained through Multiple Linear Regression for the live weights of Akkaraman lambs were less accurate than the results obtained through artificial neural network analysis.
期刊介绍:
Tropical Animal Health and Production is an international journal publishing the results of original research in any field of animal health, welfare, and production with the aim of improving health and productivity of livestock, and better utilisation of animal resources, including wildlife in tropical, subtropical and similar agro-ecological environments.