文章基本信息

标题：Prediction of the result in race walking using regularized regression models
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作者：Krzysztof Przednowek ; Krzysztof Wiktorowicz
期刊名称：Journal of Theoretical and Applied Computer Science
印刷版ISSN：2299-2634
电子版ISSN：2300-5653
出版年度：2013
卷号：7
期号：2
页码：45-58
出版社：Polska Akademia Nauk * Oddzial w Gdansku, Komisja Informatyki,Polish Academy of Sciences, Gdansk Branch, Computer Science Commission
摘要：The following paper presents the use of regularized linear models as tools to optimize training process. The models were calculated by using data collected from race-walkers' training events. The models used predict the outcomes over a 3 km race and following a prescribed training plan. The material included a total of 122 training patterns made by 21 players. The methods of analysis include: classical model of OLS regression, ridge regression, LASSO regression and elastic net regression. In order to compare and choose the best method a cross-validation of the \textit{leave-one-out} was used. All models were calculated using R language with additional packages. The best model was determined by the LASSO method which generates an error of about 26 seconds. The method has simplified the structure of the model by eliminating 5 out of 18 predictors.
关键词：regularized regression; shrinkage methods; variable selection; prediction of sport result; race walking; the R language