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As the size, complexity, and availability of data continues to grow,scientists are increasingly relying upon black-box learning algorithms that canoften provide accurate predictions with minimal a priori model specifications.Tools like random forests have an established track record of off-the-shelfsuccess and even offer various strategies for analyzing the underlyingrelationships among variables. Here, motivated by recent insights into randomforest behavior, we introduce the simple idea of augmented bagging (AugBagg), aprocedure that operates in an identical fashion to classical bagging and randomforests, but which operates on a larger, augmented space containing additionalrandomly generated noise features. Surprisingly, we demonstrate that thissimple act of including extra noise variables in the model can lead to dramaticimprovements in out-of-sample predictive accuracy, sometimes outperforming evenan optimally tuned traditional random forest. As a result, intuitive notions ofvariable importance based on improved model accuracy may be deeply flawed, aseven purely random noise can routinely register as statistically significant.Numerous demonstrations on both real and synthetic data are provided along witha proposed solution.

Getting Better from Worse: Augmented Bagging and a Cautionary Tale of Variable Importance