【摘要】 基于2020年CFPS数据，采用逻辑回归、决策树、随机森林、梯度提升决策树、支持向量机和神经网络等机器学习模型，系统分析了个体、家庭及地区特征对农民非农创业的影响，以挖掘影响农民非农创业的主要因素。研究发现，集成学习方法优于传统模型，随机森林和梯度提升决策树表现最佳。在所有特征变量中，家庭保险支出、家庭存款、土地出租、互联网接入、个体受教育程度和年龄是影响农民非农创业的主要因素。此外，偏依赖图展示了主要特征变量对农民非农创业的具体预测模式，并发现家庭保险支出、家庭存款、个体受教育程度和年龄特征对农民非农创业的影响具有显著非线性。城乡异质性分析表明，家庭保险支出和家庭存款在不同地区均显著影响非农创业；乡村地区土地流转的促进作用较突出，而城镇地区个体年龄的影响更为显著。更多还原

【Abstract】 Based on the 2020 CFPS data, this study systematically analyzes the impact of individual, household, and regional characteristics on farmers’ engagement in non-agricultural entrepreneurship, employing machine learning models such as logistic regression, decision trees, random forests, gradient boosting decision trees(GBDT), support vector machines(SVM), and neural networks. The results show that ensemble learning methods outperform traditional models, with random forests and GBDT achieving the best performance. Among all features, household insurance expenditure, household savings, land leasing, internet access, educational attainment, and age emerge as key factors influencing non-agricultural entrepreneurship. Furthermore, Partial dependence plots(PDPs) reveal the specific predictive patterns of key features for farmers’ non-agricultural entrepreneurship and highlight the significant nonlinearity in the effects of household insurance expenditure, household savings, educational attainment, and age. Analysis of urban-rural heterogeneity reveals that household insurance expenditure and savings significantly influence non-agricultural entrepreneurship across regions. Additionally, land transfer plays a more prominent role in rural areas, while individual age exerts a stronger influence in urban areas.更多还原