# FLAML: A Fast and Lightweight AutoML Library

> FLAML을 소개한 논문.

[FLAML](https://wiki.g15e.com/pages/FLAML.txt)을 소개한 논문.

## Abstract

> We study the problem of using low computational cost to automate the choices of learners and [hyperparameters](https://wiki.g15e.com/pages/Hyperparameter.txt) for an ad-hoc training dataset and error metric, by conducting trials of different configurations on the given [training data](https://wiki.g15e.com/pages/Training%20data.txt). 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](https://wiki.g15e.com/pages/FLAML.txt) 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](https://wiki.g15e.com/pages/AutoML.txt) libraries on a large open source AutoML benchmark under equal, or sometimes orders of magnitude smaller budget constraints.