  An essential task of Automated Machine Learning (AutoML) is the problem of
automatically finding the pipeline with the best generalization performance on
a given dataset. This problem has been addressed with sophisticated black-box
optimization techniques such as Bayesian Optimization, Grammar-Based Genetic
Algorithms, and tree search algorithms. Most of the current approaches are
motivated by the assumption that optimizing the components of a pipeline in
isolation may yield sub-optimal results. We present Naive AutoML, an approach
that does precisely this: It optimizes the different algorithms of a
pre-defined pipeline scheme in isolation. The finally returned pipeline is
obtained by just taking the best algorithm of each slot. The isolated
optimization leads to substantially reduced search spaces, and, surprisingly,
this approach yields comparable and sometimes even better performance than
current state-of-the-art optimizers.
