政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/142026

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Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/142026

Title:	Supervised learning for binary classification on US adult income
Authors:	陳立榜 Chen, Li-Pang
Contributors:	統計系
Keywords:	Boosting;Categorical data;Income;Discriminant analysis;Logistic regression;Prediction;Random forest;Support Vector Machine;Unbalanced binary classification
Date:	2021-12
Issue Date:	2022-09-21 11:46:06 (UTC+8)
Abstract:	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.
Relation:	Journal of Modeling and Optimization, Vol.13, No.2, pp.80-91
Data Type:	article
DOI 連結:	https://doi.org/10.32732/jmo.2021.13.2.80
DOI:	10.32732/jmo.2021.13.2.80
Appears in Collections:	[統計學系] 期刊論文

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