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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 precisely realizes such an in-isolation optimization of the different components of a pre-defined pipeline scheme. |
