FLAML: A Fast and Lightweight AutoML Library

12 Nov 2019 Chi Wang Qingyun Wu Markus Weimer Erkang Zhu

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... (read more)

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