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Arxiv
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Challenges learning from imbalanced data using tree-based models: Prevalence estimates systematically depend on hyperparameters and can be upwardly biased
- Imbalanced binary classification problems are common in various fields of study.
- Subsampling the majority class to create a balanced training dataset can bias the model's predictions.
- Calibrating a random forest model using prevalence estimates can lead to unintended negative consequences, including upwardly biased estimates.
- Random forests' prevalence estimates depend on the number of predictors considered at each split and the sampling rate used, revealing unexpected biases.
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