降低学生辍学率的机器学习方法

N. Mduma, K. Kalegele, D. Machuve
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引用次数: 11

摘要

在发展中国家,辍学是一个公认的严重问题。另一方面,机器学习技术在解决这个问题上得到了很多关注。本文以坦桑尼亚Uwezo年度学习评估数据集为例,对代表线性、集成、实例和神经网络的四种监督学习分类器进行了全面分析。该研究的目的是为当前研究该主题的研究人员提供数据驱动的算法建议。利用几何均值、f测度和调整几何均值三个指标,评估和量化了不同采样技术对不平衡数据集模型选择的影响。我们进一步指出了超参数调优在提高预测性能方面的意义。结果表明,当使用过采样技术时,逻辑回归和多层感知器两种分类器的性能最好。此外,与基线设置相比,超参数调优提高了每个算法的性能,叠加这些分类器提高了整体预测性能。
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Machine learning approach for reducing students dropout rates
School dropout is a widely recognized serious issue in developing countries. On the other hand, machine learning techniques have gained much attention on addressing this problem. This paper, presents a thorough analysis of four supervised learning classifiers that represent linear, ensemble, instance and neural networks on Uwezo Annual Learning Assessment datasets for Tanzania as a case study. The goal of the study is to provide data-driven algorithm recommendations to current researchers on the topic. Using three metrics: geometric mean, F-measure and adjusted geometric mean, we assessed and quantified the effect of different sampling techniques on the imbalanced dataset for model selection. We further indicate the significance of hyper parameter tuning in improving predictive performance. The results indicate that two classifiers: logistic regression and multilayer perceptron achieve the highest performance when over-sampling technique was employed. Furthermore, hyper parameter tuning improves each algorithm's performance compared to its baseline settings and stacking these classifiers improves the overall predictive performance.
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