期刊名称：International Journal of Information Technology and Computer Science
印刷版ISSN：2074-9007
电子版ISSN：2074-9015
出版年度：2019
卷号：11
期号：9
页码：1-8
DOI：10.5815/ijitcs.2019.09.01
出版社：MECS Publisher
摘要：This paper studies the forecasting mechanism of the most widely used machine learning algorithms, namely linear discriminant analysis, logistic regression, k-nearest neighbors, random forests, artificial neural network, naive Bayes, classification and regression trees, support vector machines, adaptive boosting, and stacking ensemble model, in forecasting first-generation college students’ six-year graduation using the first college year’s data. Five standard evaluating metrics are used to evaluate these models. The results show that these machine learning models can significantly predict first-generation college students’ six-year graduation with mean forecasting accuracy rate spanning from 69.58% to 75.17% and median forecasting accuracy rate spanning from 70.37% to 74.52%. Among these machine learning algorithms, stacking ensemble model, logistic regression model, and linear discriminant analysis are the best three ones in terms of mean forecasting accuracy rate. Furthermore, the results from the repeated ten-fold cross-validation process reveal that the variations of the five evaluating metrics exhibit remarkably different patterns across the ten machine learning algorithms.
关键词：Machine learning algorithms;first-generation college students;six-year graduation;forecasting evaluation
