This release contains an important new feature: zero-shot AutoML and mete learning. It provides a new way of doing AutoML without tuning. You can now use the existing training API from lightgbm, xgboost etc. while getting the benefit of AutoML in choosing high-performance hyperparameter configurations per task. Recommended for everyone currently using lightgbm, xgboost or random forest, regardless of previous experience in AutoML. This feature also enables continuous improvement of AutoML from historical AutoML experiments.
Other changes can be found below.
What's Changed
- Typo on the webpage's Getting Started section by @cammarb in #457
- Bump follow-redirects from 1.14.7 to 1.14.8 in /website by @sonichi in #459
- Docstr update by @qingyun-wu in #460
- update regression metrics in notebooks by @sonichi in #454
- make AutoML.classes_ an array by @sonichi in #467
- Bump prismjs from 1.25.0 to 1.27.0 in /website by @sonichi in #471
- Zero-shot AutoML by @sonichi in #468
- don't init global search with points_to_evaluate unless evaluated_rewards is provided; handle callbacks in fit kwargs by @sonichi in #469
New Contributors
Full Changelog: v0.9.7...v0.10.0