  Much of the work in metalearning has focused on classifier selection,
combined more recently with hyperparameter optimization, with little concern
for data preprocessing. Yet, it is generally well accepted that machine
learning applications require not only model building, but also data
preprocessing. In other words, practical solutions consist of pipelines of
machine learning operators rather than single algorithms. Interestingly, our
experiments suggest that, on average, data preprocessing hinders accuracy,
while the best performing pipelines do actually make use of preprocessors.
Here, we conduct an extensive empirical study over a wide range of learning
algorithms and preprocessors, and use metalearning to determine when one should
make use of preprocessors in ML pipeline design.
