{"title":"利用机器学习预测印度农业本科学生创业特征","authors":"S. Jarial, Jayant Verma","doi":"10.1108/jadee-06-2022-0124","DOIUrl":null,"url":null,"abstract":"PurposeThis study aimed to understand the agri-entrepreneurial traits of undergraduate university students using machine learning (ML) algorithms.Design/methodology/approachThis study used a conceptual framework of individual-level determinants of entrepreneurship and ML. The Google Survey instrument was prepared on a 5-point scale and administered to 656 students in different sections of the same class during regular virtual classrooms in 2021. The datasets were analyzed and compared using ML.FindingsEntrepreneurial traits existed among students before attending undergraduate entrepreneurship courses. Establishing strong partnerships (0.359), learning (0.347) and people-organizing ability (0.341) were promising correlated entrepreneurial traits. Female students exhibited fewer entrepreneurial traits than male students. The random forest model exhibited 60% accuracy in trait prediction against gradient boosting (58.4%), linear regression (56.8%), ridge (56.7%) and lasso regression (56.0%). Thus, the ML model appeared to be unsuitable to predict entrepreneurial traits. Quality data are important for accurate trait predictions.Research limitations/implicationsFurther studies can validate K-nearest neighbors (KNN) and support vector machine (SVM) models against random forest to support the statement that the ML model cannot be used for entrepreneurial trait prediction.Originality/valueThis research is unique because ML models, such as random forest, gradient boosting and lasso regression, are used for entrepreneurial trait prediction by agricultural domain students.","PeriodicalId":45976,"journal":{"name":"Journal of Agribusiness in Developing and Emerging Economies","volume":" ","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2023-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prognosis of entrepreneurial traits among agricultural undergraduate students in India using machine learning\",\"authors\":\"S. Jarial, Jayant Verma\",\"doi\":\"10.1108/jadee-06-2022-0124\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"PurposeThis study aimed to understand the agri-entrepreneurial traits of undergraduate university students using machine learning (ML) algorithms.Design/methodology/approachThis study used a conceptual framework of individual-level determinants of entrepreneurship and ML. The Google Survey instrument was prepared on a 5-point scale and administered to 656 students in different sections of the same class during regular virtual classrooms in 2021. The datasets were analyzed and compared using ML.FindingsEntrepreneurial traits existed among students before attending undergraduate entrepreneurship courses. Establishing strong partnerships (0.359), learning (0.347) and people-organizing ability (0.341) were promising correlated entrepreneurial traits. Female students exhibited fewer entrepreneurial traits than male students. The random forest model exhibited 60% accuracy in trait prediction against gradient boosting (58.4%), linear regression (56.8%), ridge (56.7%) and lasso regression (56.0%). Thus, the ML model appeared to be unsuitable to predict entrepreneurial traits. Quality data are important for accurate trait predictions.Research limitations/implicationsFurther studies can validate K-nearest neighbors (KNN) and support vector machine (SVM) models against random forest to support the statement that the ML model cannot be used for entrepreneurial trait prediction.Originality/valueThis research is unique because ML models, such as random forest, gradient boosting and lasso regression, are used for entrepreneurial trait prediction by agricultural domain students.\",\"PeriodicalId\":45976,\"journal\":{\"name\":\"Journal of Agribusiness in Developing and Emerging Economies\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2023-02-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Agribusiness in Developing and Emerging Economies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1108/jadee-06-2022-0124\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"AGRICULTURAL ECONOMICS & POLICY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Agribusiness in Developing and Emerging Economies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/jadee-06-2022-0124","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AGRICULTURAL ECONOMICS & POLICY","Score":null,"Total":0}
Prognosis of entrepreneurial traits among agricultural undergraduate students in India using machine learning
PurposeThis study aimed to understand the agri-entrepreneurial traits of undergraduate university students using machine learning (ML) algorithms.Design/methodology/approachThis study used a conceptual framework of individual-level determinants of entrepreneurship and ML. The Google Survey instrument was prepared on a 5-point scale and administered to 656 students in different sections of the same class during regular virtual classrooms in 2021. The datasets were analyzed and compared using ML.FindingsEntrepreneurial traits existed among students before attending undergraduate entrepreneurship courses. Establishing strong partnerships (0.359), learning (0.347) and people-organizing ability (0.341) were promising correlated entrepreneurial traits. Female students exhibited fewer entrepreneurial traits than male students. The random forest model exhibited 60% accuracy in trait prediction against gradient boosting (58.4%), linear regression (56.8%), ridge (56.7%) and lasso regression (56.0%). Thus, the ML model appeared to be unsuitable to predict entrepreneurial traits. Quality data are important for accurate trait predictions.Research limitations/implicationsFurther studies can validate K-nearest neighbors (KNN) and support vector machine (SVM) models against random forest to support the statement that the ML model cannot be used for entrepreneurial trait prediction.Originality/valueThis research is unique because ML models, such as random forest, gradient boosting and lasso regression, are used for entrepreneurial trait prediction by agricultural domain students.
期刊介绍:
The Journal of Agribusiness in Developing and Emerging Economies publishes double-blind peer-reviewed research on issues relevant to agriculture and food value chain in emerging economies in Asia, Africa, Latin America and Eastern Europe. The journal welcomes original research, particularly empirical/applied, quantitative and qualitative work on topics pertaining to policies, processes, and practices in the agribusiness arena in emerging economies to inform researchers, practitioners and policy makers