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Forget holdout data for feature effect estimation: training data's larger sample size usually wins, and cross-validation can further reduce model variance.
Unlock robust feature importance analysis with `xplainfi`, an R package that fills critical gaps by offering conditional importance methods and statistical inference for diverse ML models.
Questioning the common practice of interpreting data through a single model class, this work reveals the existence of alternative well-performing models across multiple model classes and their hyperparameters.
