Examining variable selection methods for the predictive performance of regression models and the proportion of selected variables and selected random variables
The selection of a descriptor, X, is crucial for improving the interpretation and prediction accuracy of a regression model. In this study, the prediction accuracy of models constructed using the selected X was determined and the results of variable selection, according to the number of selected X a...
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| Format: | Article |
| Language: | English |
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Elsevier
2021-06-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844021014596 |
| _version_ | 1819145880072093696 |
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| author | Hiromasa Kaneko |
| author_facet | Hiromasa Kaneko |
| author_sort | Hiromasa Kaneko |
| collection | DOAJ |
| description | The selection of a descriptor, X, is crucial for improving the interpretation and prediction accuracy of a regression model. In this study, the prediction accuracy of models constructed using the selected X was determined and the results of variable selection, according to the number of selected X and number of selected variables that are unrelated to an objective variable, such as activities and properties (y), were investigated to evaluate the variable or feature selection methods. Variable selection methods include least absolute shrinkage and selection operator, genetic algorithm-based partial least squares, genetic algorithm-based support vector regression, and Boruta. Several regression analysis methods were used to test the prediction accuracy of the model constructed using the selected X. The characteristics of each variable selection method were analyzed using eight datasets. The results showed that even when variables unrelated to y were selected by variable selection and the number of unrelated variables was the same as the number of the original variables, a regression model with good accuracy, which ignores the influence of such noise variables, can be constructed by applying various regression analysis methods. Additionally, the variables related to y must not to be deleted. These findings provide a basis for improving the variable selection methods. |
| first_indexed | 2024-12-22T13:05:03Z |
| format | Article |
| id | doaj.art-ad4d2edcf0b14610b1254a80da21f96e |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-12-22T13:05:03Z |
| publishDate | 2021-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-ad4d2edcf0b14610b1254a80da21f96e2022-12-21T18:24:55ZengElsevierHeliyon2405-84402021-06-0176e07356Examining variable selection methods for the predictive performance of regression models and the proportion of selected variables and selected random variablesHiromasa Kaneko0Corresponding author.; Department of Applied Chemistry, School of Science and Technology, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki, Kanagawa 214-8571, JapanThe selection of a descriptor, X, is crucial for improving the interpretation and prediction accuracy of a regression model. In this study, the prediction accuracy of models constructed using the selected X was determined and the results of variable selection, according to the number of selected X and number of selected variables that are unrelated to an objective variable, such as activities and properties (y), were investigated to evaluate the variable or feature selection methods. Variable selection methods include least absolute shrinkage and selection operator, genetic algorithm-based partial least squares, genetic algorithm-based support vector regression, and Boruta. Several regression analysis methods were used to test the prediction accuracy of the model constructed using the selected X. The characteristics of each variable selection method were analyzed using eight datasets. The results showed that even when variables unrelated to y were selected by variable selection and the number of unrelated variables was the same as the number of the original variables, a regression model with good accuracy, which ignores the influence of such noise variables, can be constructed by applying various regression analysis methods. Additionally, the variables related to y must not to be deleted. These findings provide a basis for improving the variable selection methods.http://www.sciencedirect.com/science/article/pii/S2405844021014596Variable selectionFeature selectionRegressionPredictive accuracyInterpretabilityQSPR |
| spellingShingle | Hiromasa Kaneko Examining variable selection methods for the predictive performance of regression models and the proportion of selected variables and selected random variables Heliyon Variable selection Feature selection Regression Predictive accuracy Interpretability QSPR |
| title | Examining variable selection methods for the predictive performance of regression models and the proportion of selected variables and selected random variables |
| title_full | Examining variable selection methods for the predictive performance of regression models and the proportion of selected variables and selected random variables |
| title_fullStr | Examining variable selection methods for the predictive performance of regression models and the proportion of selected variables and selected random variables |
| title_full_unstemmed | Examining variable selection methods for the predictive performance of regression models and the proportion of selected variables and selected random variables |
| title_short | Examining variable selection methods for the predictive performance of regression models and the proportion of selected variables and selected random variables |
| title_sort | examining variable selection methods for the predictive performance of regression models and the proportion of selected variables and selected random variables |
| topic | Variable selection Feature selection Regression Predictive accuracy Interpretability QSPR |
| url | http://www.sciencedirect.com/science/article/pii/S2405844021014596 |
| work_keys_str_mv | AT hiromasakaneko examiningvariableselectionmethodsforthepredictiveperformanceofregressionmodelsandtheproportionofselectedvariablesandselectedrandomvariables |