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- W2964305932 abstract "Abstract A frequent problem in binary classification is class imbalance between a minority and a majority class such as defaults and non-defaults in default prediction. In this article, we introduce a novel binary classification model, the Grabit model, which is obtained by applying gradient tree boosting to the Tobit model. We show how this model can leverage auxiliary data to obtain increased predictive accuracy for imbalanced data. We apply the Grabit model to predicting defaults on loans made to Swiss small and medium-sized enterprises (SME) and obtain a large and significant improvement in predictive performance compared to other state-of-the-art approaches." @default.
- W2964305932 created "2019-07-30" @default.
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- W2964305932 date "2019-05-01" @default.
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- W2964305932 title "Grabit: Gradient tree-boosted Tobit models for default prediction" @default.
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- W2964305932 doi "https://doi.org/10.1016/j.jbankfin.2019.03.004" @default.
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