利用机器学习预测印度农业本科学生创业特征

IF 2.4 Q2 AGRICULTURAL ECONOMICS & POLICY
S. Jarial, Jayant Verma
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引用次数: 0

摘要

目的利用机器学习(ML)算法了解大学生的农业创业特征。本研究使用了创业精神和ML的个人层面决定因素的概念框架。谷歌调查工具以5分制编制,并于2021年在常规虚拟教室中对同一班级不同部门的656名学生进行了管理。结果表明,大学生在参加本科创业课程前存在创业特质。建立牢固的伙伴关系(0.359)、学习能力(0.347)和组织能力(0.341)是有前景的相关创业特征。女生比男生表现出更少的创业特质。随机森林模型对梯度增强(58.4%)、线性回归(56.8%)、脊回归(56.7%)和套索回归(56.0%)的预测准确率为60%。因此,ML模型似乎不适合预测创业特征。高质量的数据对于准确的性状预测非常重要。进一步的研究可以验证k近邻(KNN)和支持向量机(SVM)模型对随机森林的影响,以支持机器学习模型不能用于创业特质预测的说法。原创性/价值本研究的独特之处在于ML模型,如随机森林、梯度提升和套索回归,被农业领域的学生用于创业特质预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
CiteScore
4.60
自引率
37.50%
发文量
58
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
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