Generative data augmentation with adversarial random forests raises logistic regression accuracy from 0.70 to 0.81 and AUC from 0.85 to 0.92 for post-operative discharge prediction in geriatric patients, while random forest and TabPFN remain largely unchanged.
Missing Value Imputation With Adversarial Random Forests—MissARF
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Improving post-operative discharge destination prediction of geriatric patients with generative data augmentation
Generative data augmentation with adversarial random forests raises logistic regression accuracy from 0.70 to 0.81 and AUC from 0.85 to 0.92 for post-operative discharge prediction in geriatric patients, while random forest and TabPFN remain largely unchanged.