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• W4285800002 abstract "Random forest is a popular prediction approach for handling high dimensional covariates. However, it often becomes infeasible to interpret the obtained high dimensional and non-parametric model. Aiming for an interpretable predictive model, we develop a forward variable selection method using the continuous ranked probability score (CRPS) as the loss function. eOur stepwise procedure selects at each step a variable that minimizes the CRPS risk and a stopping criterion for selection is designed based on an estimation of the CRPS risk difference of two consecutive steps. We provide mathematical motivation for our method by proving that in a population sense, the method attains the optimal set. In a simulation study, we compare the performance of our method with an existing variable selection method, for different sample sizes and correlation strength of covariates. Our method is observed to have a much lower false positive rate. We also demonstrate an application of our method to statistical post-processing of daily maximum temperature forecasts in the Netherlands. Our method selects about 10% covariates while retaining the same predictive power." @default.
• W4285800002 created "2022-07-19" @default.
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• W4285800002 date "2022-07-18" @default.
• W4285800002 modified "2023-10-01" @default.
• W4285800002 title "Forward variable selection for random forest models" @default.
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• W4285800002 doi "https://doi.org/10.1080/02664763.2022.2095362" @default.
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