AutoML 算法性能比较分析:私立大学学生欠费情况分类模型

Henry Villarreal-Torres, Julio C. Angeles-Morales, Jenny E. Cano-Mejía, Carmen Mejía-Murillo, Gumercindo Flores-Reyes, Oscar Cruz-Cruz, Manuel Urcia-Quispe, Manuel Palomino-Márquez, Miguel Solar-Jara, Reyna Escobedo-Zarzosa
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引用次数: 0

摘要

人工智能对我们社会的影响是重要的,因为通过数据科学的过程创新,了解导致大学生逾期付款的学术和社会人口因素,识别这些因素并及时做出决策,实施预防和纠正计划,避免学生因这一经济问题而辍学,并确保他们以有意义和专注的方式在教育中取得成功。在这个意义上,本研究的目的是通过在包含8,495条记录的数据集上使用AutoKeras、AutoGluon、HyperOPT、MLJar和H2O等现有各种平台和解决方案的AutoML算法,并应用数据平衡技术,比较某私立大学学生逾期付款分类模型的性能指标。从各种算法的实现和执行情况来看,基于各个工具自动使用的参数和优化函数得到了相似的指标,通过指标精度= 0.778的Stacked Ensemble算法为H2O平台提供了更好的性能。F1 = 0.870,召回率= 0.904,精度= 0.839。由于对自动化机器学习的兴趣日益浓厚,该研究可以扩展到其他背景或知识领域,为研究人员提供了一个有价值的数据科学工具,而不需要深入的知识。
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Comparative analysis of performance of AutoML algorithms: Classification model of payment arrears in students of a private university
The impact of artificial intelligence in our society is important due to the innovation of processes through data science to know the academic and sociodemographic factors that contribute to late payments in university students, to identify them and make timely decisions for implementing prevention and correction programs, avoiding student dropout due to this economic problem, and ensuring success in their education in a meaningful and focused way. In this sense, the research aims to compare the performance metrics of classification models for late payments in students of a private university by using AutoML algorithms from various existing platforms and solutions such as AutoKeras, AutoGluon, HyperOPT, MLJar, and H2O in a data set consisting of 8,495 records and the application of data balancing techniques. From the implementation and execution of various algorithms, similar metrics have been obtained based on the parameters and optimization functions used automatically by each tool, providing better performance to the H2O platform through the Stacked Ensemble algorithm with metrics accuracy = 0.778. F1 = 0.870, recall = 0.904 and precision = 0.839. The research can be extended to other contexts or areas of knowledge due to the growing interest in automated machine learning, providing researchers with a valuable tool in data science without the need for deep knowledge.
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