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.
2.4.1
2.4.0
Using quantile regression, we can now return prediction intervals.
Another minor change is adding in a column of absolute changes for feature_responses
2.3.5
LightGBM and sklearn's gbm now use warm_starting or iterative training to find the best number of trees
2.2.1
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.
2.2.0
Feature responses allows linear-model-like interpretations for non-linear models.