Всем привет! Вышел CatBoost 1.2.1. New features Allow

to optimize specific ranking loss functions with YetiRank and YetiRankPairwise by specifying mode parameter. See Which Tricks are Important for Learning to Rank? paper for details (this family of losses is called YetiLoss there). CPU-only for now.
Add Kernel Gradient Boosting support (use catboost.sample_gaussian_process function). #2408, thanks to @TakeOver. See Gradient Boosting Performs Gaussian Process Inference paper for details.
LambdaMart loss: support new target metrics MRR, ERR and MAP.
StochasticRank loss: support new target metrics ERR and MRR.
Support MultiRMSE on GPU. #2264, #2390
Load JSON model format in Java Client. #1627, thanks to @timotta
Implement exporting of Multiclass models to C++ and Python. #2283, thanks to @antoninkriz

Improvements

Speedup BM25 feature calcers 3x
Use int instead of deprecated numpy.int. #2378
Add ModelCalcerWrapper::CalcFlatTransposed, #2413 thanks to @faucct
Update dependencies to avoid known vulnerabilities

Bugfixes

Fix __shfl_up_sync mask. #2339
TFocalMetric negative values fix. #2386, thanks to @diditforlulz273
Focal loss: Use user-defined alpha and gamma
Fix exception propagation: Rethrow exceptions caused by user's python code as C++ exceptions
CatBoost trained with user defined objective was incompatible with ShapValues calculation
Avoid nan's in Newton step calculation for RMSEWithUncertainty
Fix score method for y with shape (N, 1). #2405
Fix scalePosWeight support for Spark. #2470

Что такое Kernel Gradient Boosting? Есть ли какие-то примеры/код для чего это может использоваться на практике?

https://arxiv.org/abs/2206.05608