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- W1983281014 abstract "AbstractMethamphetamine use among homeless youths is an increasing problem. School officials and social work practitioners are presented with a classification problem in determining which youth are or are not using methamphetamines. The purpose of this study is to adopt a machine-learning approach to address this type of classification problem and to compare 2 models (decision rules and logistic regression) for classifying cases into methamphetamine users and nonusers. The selection of predictors in our models was guided by the risk and resilience framework. Logistic regression and decision rules analyses are used to test the models with a subset of data for 2,146 homeless youth who attend high school that was obtained from the 2007–08 California Healthy Kids Survey dataset. Results of logistic regression suggest methamphetamine use is associated with cigarette and marijuana use, having consumed alcohol, and being truant more than once per week. Results of decision rules analysis suggest a youth’s being c..." @default.
- W1983281014 created "2016-06-24" @default.
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- W1983281014 date "2014-06-01" @default.
- W1983281014 modified "2023-09-24" @default.
- W1983281014 title "Predicting Methamphetamine Use of Homeless Youths Attending High School: Comparison of Decision Rules and Logistic Regression Classification Algorithms" @default.
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- W1983281014 doi "https://doi.org/10.1086/676830" @default.
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