Naive Automated Machine Learning
This work addresses the challenge of reducing search complexity in AutoML for practitioners, though it is incremental as it builds on existing pipeline optimization methods.
The paper tackles the problem of automatically finding the best machine learning pipeline for a given dataset by proposing Naive AutoML, which optimizes pipeline components in isolation, and shows that this approach yields comparable or sometimes better performance than state-of-the-art optimizers.
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.