FLAML: A Fast and Lightweight AutoML Library
This provides a more efficient AutoML solution for users with limited computational resources, though it is incremental in improving existing methods.
The authors tackled the problem of automating machine learning model and hyperparameter selection with low computational cost, resulting in FLAML, a library that outperforms top AutoML libraries under equal or smaller budget constraints.
We study the problem of using low computational cost to automate the choices of learners and hyperparameters for an ad-hoc training dataset and error metric, by conducting trials of different configurations on the given training data. We investigate the joint impact of multiple factors on both trial cost and model error, and propose several design guidelines. Following them, we build a fast and lightweight library FLAML which optimizes for low computational resource in finding accurate models. FLAML integrates several simple but effective search strategies into an adaptive system. It significantly outperforms top-ranked AutoML libraries on a large open source AutoML benchmark under equal, or sometimes orders of magnitude smaller budget constraints.