[UNMAINTAINED] Automated machine learning for analytics & production
Ensembling's back for it's alpha release, evolutionary algorithms are doing our hyperparameter search now, we've handled a bunch of dependency updates, and a bunch of smaller performance tweaks.
Using quantile regression, we can now return prediction intervals.
Another minor change is adding in a column of absolute changes for feature_responses
LightGBM and sklearn's gbm now use warm_starting or iterative training to find the best number of trees
Avoids double training deep learning models, changes how we sort and order features for analytics reporting, and adds a new _all_small_categories
category to categorical ensembling.
Feature responses allows linear-model-like interpretations for non-linear models.
Avoids mutating input DF
Standardizes examples and tests to use load_ml_model()
Some bugfixes
Feature learning and categorical ensembling are really cool features that each get us 2-5% accuracy gains!
For full info, check the docs.
Enough incremental improvements have added up that we're now ready to mark a 2.0 release!
Part of the progress also means deprecating a few unused features that were adding unnecessary complexity and preventing us from implementing new features like ensembling properly.
New changes for the 2.0 release:
Major changes since the 1.0 release: