<commit_before>"""
The module contains an implementation of uBoost algorithm.
The main goal of **uBoost** is to fight correlation between predictions and some variables (i.e. mass of particle).

* `uBoostBDT` is a modified version of AdaBoost, that targets to obtain efficiency uniformity at the specified level (global efficiency)
* `uBoostClassifier` is a combination of uBoostBDTs for different efficiencies

This implementation is more advanced than one described in the original paper,
contains smoothing and trains classifiers in threads, has `learning_rate` and `uniforming_rate` parameters
and supports SAMME.R modification to use predicted probabilities.<commit_msg>minor doc change<commit_after>"""
The module contains an implementation of uBoost algorithm.
The main goal of **uBoost** is to fight correlation between predictions and some variables (i.e. mass of particle).

* `uBoostBDT` is a modified version of AdaBoost, that targets to obtain efficiency uniformity at the specified level (global efficiency)
* `uBoostClassifier` is a combination of uBoostBDTs for different efficiencies

This implementation is more advanced than one described in the original paper,
contains smoothing and trains classifiers in threads, has `learning_rate` and `uniforming_rate` parameters,
does automatic weights renormalization and supports SAMME.R modification to use predicted probabilities.
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