Safety vulnerability ID: 41743
The information on this page was manually curated by our Cybersecurity Intelligence Team.
Catboost 0.26 updates version of 'scala' to v2.11.12 for security reasons.
https://github.com/catboost/catboost/issues/1632
Latest version: 1.2.7
CatBoost Python Package
New features
* 972. Add model evaluation on GPU. Thanks to rakalexandra.
* Support Langevin on GPU
* Save class labels to models in cross validation
* 1524. Return models after CV. Thanks to vklyukin
* [Python] 766. Add CatBoostRanker & pool.get_group_id_hash() for ranking. Thanks to AnnaAraslanova
* 262. Make CatBoost widget work in jupyter lab. Thanks to Dm17r1y
* [GPU only] Allow to add exponent to score aggregation function
* Allow to specify threshold parameter for binary classification model. Thanks to Keksozavr.
* [C Model API] 503. Allow to specify prediction type.
* [C Model API] 1201. Get predictions for a specific class.
Breaking changes
* Use CUDA 11 by default. CatBoost GPU now requires Linux x86_64 Driver Version >= 450.51.06 Windows x86_64 Driver Version >= 451.82.
Losses and metrics
* Add MRR and ERR metrics on CPU.
* Add [LambdaMart](https://www.microsoft.com/en-us/research/publication/from-ranknet-to-lambdarank-to-lambdamart-an-overview/) loss.
* 1557. Add survivalAFT base logic. Thanks to blatr.
* 1286. Add Cox Proportional Hazards Loss. Thanks to fibersel.
* 1595. Provide object-oriented interface for setting up metric parameters. Thanks to ks-korovina.
* Change default YetiRank decay to 0.85 for better quality.
Python package
* 1372. Custom logging stream in python package. Thanks to DianaArapova.
* 1304. Callback after iteration functionality. Thanks to qoter.
R package
* 251. Train parameter synonyms. Thanks to ebalukova.
* 252. Add `eval_metrics`. Thanks to ebalukova.
Speedups
* [Python] Speed up custom metrics and objectives with `numba` (if available)
* [Python] 1710. Large speedup for cv dataset splitting by sklearn splitter
Other
* Use Exact leaves estimation method as default on GPU
* [Spark] 1632. Update version of Scala 2.11 for security reasons.
* [Python] 1695. Explicitly specify WHEEL 'Root-Is-Purelib' value
Bugfixes
* Fix default projection dimension for embeddings
* Fix `use_weights` for some eval_metrics on GPU - `use_weights=False` is always respected now
* [Spark] 1649. The earlyStoppingRounds parameter is not recognized
* [Spark] 1650. Error when using the autoClassWeights parameter
* [Spark] 1651. Error about "Auto-stop PValue" when using odType "Iter" and odWait
* Fix usage of pairlogit weights for CPU fallback metrics when training on GPU
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