automl
Automated Feature Engineering
3260 papers • 126 benchmarks • 313 datasets
Automated feature engineering improves upon the traditional approach to feature engineering by automatically extracting useful and meaningful features from a set of related data tables with a framework that can be applied to any problem.
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Most implemented papers
Benchmarking Automatic Machine Learning Frameworks
Adithya Balaji, A. Allen•Thu Aug 16 2018
This work tests auto-sklearn, TPOT, auto_ml, and H2O's AutoML solution against a compiled set of regression and classification datasets sourced from OpenML and finds that auto-Sklearn performs the best across classification datasets and TPOT performs thebest across regression datasets.
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