Articles | Volume 25, issue 9
https://doi.org/10.5194/hess-25-4947-2021
https://doi.org/10.5194/hess-25-4947-2021
Research article
 | 
09 Sep 2021
Research article |  | 09 Sep 2021

Development of a Wilks feature importance method with improved variable rankings for supporting hydrological inference and modelling

Kailong Li, Guohe Huang, and Brian Baetz

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Latest update: 26 Apr 2024
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Short summary
We proposed a test statistic feature importance method to quantify the importance of predictor variables for random-forest-like models. The proposed method does not rely on any performance measures to evaluate variable rankings, which can thus result in unbiased variable rankings. The resulting variable rankings based on the proposed method could help random forest achieve its optimum predictive accuracy.