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Feature bagging is a well-established ensembling method which aims to reduceprediction variance by combining predictions of many estimators trained onsubsets or projections of features. Here, we develop a theory offeature-bagging in noisy least-squares ridge ensembles and simplify theresulting learning curves in the special case of equicorrelated data. Usinganalytical learning curves, we demonstrate that subsampling shifts thedouble-descent peak of a linear predictor. This leads us to introduceheterogeneous feature ensembling, with estimators built on varying numbers offeature dimensions, as a computationally efficient method to mitigatedouble-descent. Then, we compare the performance of a feature-subsamplingensemble to a single linear predictor, describing a trade-off between noiseamplification due to subsampling and noise reduction due to ensembling. Ourqualitative insights carry over to linear classifiers applied to imageclassification tasks with realistic datasets constructed using astate-of-the-art deep learning feature map.

Learning Curves for Noisy Heterogeneous Feature-Subsampled Ridge Ensembles