  This study compares various superlearner and deep learning architectures
(machine-learning-based and neural-network-based) for classification problems
across several simulated and industrial datasets to assess performance and
computational efficiency, as both methods have nice theoretical convergence
properties. Superlearner formulations outperform other methods at small to
moderate sample sizes (500-2500) on nonlinear and mixed linear/nonlinear
predictor relationship datasets, while deep neural networks perform well on
linear predictor relationship datasets of all sizes. This suggests faster
convergence of the superlearner compared to deep neural network architectures
on many messy classification problems for real-world data.
  Superlearners also yield interpretable models, allowing users to examine
important signals in the data; in addition, they offer flexible formulation,
where users can retain good performance with low-computational-cost base
algorithms.
  K-nearest-neighbor (KNN) regression demonstrates improvements using the
superlearner framework, as well; KNN superlearners consistently outperform deep
architectures and KNN regression, suggesting that superlearners may be better
able to capture local and global geometric features through utilizing a variety
of algorithms to probe the data space.
