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    题名: Supervised learning for binary classification on US adult income
    作者: 陳立榜
    Chen, Li-Pang
    贡献者: 統計系
    关键词: Boosting;Categorical data;Income;Discriminant analysis;Logistic regression;Prediction;Random forest;Support Vector Machine;Unbalanced binary classification
    日期: 2021-12
    上传时间: 2022-09-21 11:46:06 (UTC+8)
    摘要: In this project, various binary classification methods have been used to make predictions about US adult income level in relation to social factors including age, gender, education, and marital status. We first explore descriptive statistics for the dataset and deal with missing values. After that, we examine some widely used classification methods, including logistic regression, discriminant analysis, support vector machine, random forest, and boosting. Meanwhile, we also provide suitable R functions to demonstrate applications. Various metrics such as ROC curves, accuracy, recall and F-measure are calculated to compare the performance of these models. We find the boosting is the best method in our data analysis due to its highest AUC value and the highest prediction accuracy. In addition, among all predictor variables, we also find three variables that have the largest impact on the US adult income level.
    關聯: Journal of Modeling and Optimization, Vol.13, No.2, pp.80-91
    数据类型: article
    DOI 連結: https://doi.org/10.32732/jmo.2021.13.2.80
    DOI: 10.32732/jmo.2021.13.2.80
    显示于类别:[統計學系] 期刊論文

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