Naive Automated Machine Learning

TL;DR

Naive AutoML simplifies the pipeline optimization process by independently selecting the best algorithms for each component, resulting in a reduced search space and competitive performance compared to more complex methods.

Contribution

It introduces a naive approach that optimizes pipeline components in isolation, challenging the assumption that joint optimization is always necessary.

Findings

Naive AutoML achieves comparable or better results than state-of-the-art methods.

The approach significantly reduces search space complexity.

Independent component optimization can be surprisingly effective.

Abstract

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.