o https://github.com/Featuretools/featuretools - Automated feature engineering with main focus on relational structures and deep feature synthesis
o https://github.com/blue-yonder/tsfresh - Automatic extraction of relevant features from time series
o https://github.com/machinalis/featureforge - creating and testing machine learning features, with a scikit-learn compatible API
o https://github.com/asavinov/lambdo - Feature engineering and machine learning: together at last! The workflow engine allows for integrating feature training and data wrangling tasks with conventional ML
o https://github.com/xiaoganghan/awesome-feature-engineering - other resource related to feature engineering (video, audio, text)
Breaks 70% accuracy on the Winograd schema for the first time! (a lazy 7% improvement in performance....)
I'd be interested to hear what kind of experience people are having with these frameworks in production.
o https://github.com/Featuretools/featuretools - Automated feature engineering with main focus on relational structures and deep feature synthesis
o https://github.com/blue-yonder/tsfresh - Automatic extraction of relevant features from time series
o https://github.com/machinalis/featureforge - creating and testing machine learning features, with a scikit-learn compatible API
o https://github.com/asavinov/lambdo - Feature engineering and machine learning: together at last! The workflow engine allows for integrating feature training and data wrangling tasks with conventional ML
o https://github.com/xiaoganghan/awesome-feature-engineering - other resource related to feature engineering (video, audio, text)