Local sample weighting inside random-forest splits and neural-network mini-batches sharpens feature importance under feature correlation and improves out-of-distribution accuracy in simulations.
On the impor- tance of interpretable machine learning predictions to inform clinical decision making in oncology,
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Decorrelated feature importance from local sample weighting
Local sample weighting inside random-forest splits and neural-network mini-batches sharpens feature importance under feature correlation and improves out-of-distribution accuracy in simulations.