methodology-6
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.
(Image credit: Papersgraph)
Benchmarks
These leaderboards are used to track progress in automated-feature-engineering-6
Trend
Dataset
Best Model
Actions
No benchmarks available.
Libraries
iUse these libraries to find automated-feature-engineering-6 models and implementations
Datasets
No datasets available.
Subtasks
No subtasks available.
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.
77