Randomness from model optimization and initialization causes individual risk predictions in flexible ML models to vary as much as resampling the full training data, potentially flipping clinical decisions near thresholds.
Different horses for different courses: Comparing bias mitigation algorithms in ml
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Diagnostics for Individual-Level Prediction Instability in Machine Learning for Healthcare
Randomness from model optimization and initialization causes individual risk predictions in flexible ML models to vary as much as resampling the full training data, potentially flipping clinical decisions near thresholds.