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2.7.0

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.

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