#catboost_tipsntricks CatBoost sets a learning rate by looking at the number of iterations&objects in the trainset. In today's video, Nikita explains how to use built-in interactive learning curves to tune LR & iterations and improve model performance.

#catboost_tipsntricks Consoles are not only for Jupyter&Python😸 In today's video Kate explains how to use main CatBoost features from CLI. This simple but powerful interface allows you to use practically anywhere and improve ml pipelines.

RT @AbozeBrain: Gradient boosting methods have been proven to be an important strategy. This article with @neptune_ai aims to investigate….

#catboost_tipsntricks 📹Model prediction interpretation in a human-readable form is a key for making a great machine learning system. In this video Nikita shows how to use SHAP values to understand model predictions

Technical notice⚠️ In the next release, we will stop publishing CatBoost artifacts for Python 2.7 & 3.5 versions. If you still need CatBoost built for 2.7 or 3.5 - you can build it from sources. If you have any questions - contact us here, in telegram or via GitHub issues!😺.

#catboost_tipsntricks Feature selection is a crucial part of data engineering & ML. In today's video, Ivan talks about CatBoost's built-in feature selection function. It can help you speed up training and reduce overfitting.🚀

#catboost_tipsntricks If you use GBDT models in production, don't miss that video😺 Ekaterina Ermishkina explains how to apply CatBoost models in different formats and environments: native binary format, CoreML, PMML, ONNX, in Java, Rust, NodeJS and others

#catboost_tipsntricks In today's video, Nikita Dmitriev talks about object importance and how you can use it to detect and drop noise objects and boost the quality of your models🚀Stay tuned for the next episode! 😺

We've recorded a series of short videos to boost your CatBoost knowledge, so stay tuned😺.In today's video, Ivan Lyzhin explains why you should try different tree grow policies.

See full release notes in our github 😺🥳.

And let us introduce the main features: fully distributed training for Apache Spark, multilabel multiclassification, big CPU training time speedup (up to 35%), improved CV speed, LogCosh loss, model size regularization fix for GPU, markdown documentation. And that's not all!.

🚀🎇1.0.0😺🥳.It's not only CatBoost's 100₂ anniversary but also a first major release! We upgraded the major version because CatBoost looks solid: by the last four years, CatBoost began to play a crucial role in Yandex, CERN, and many other companies.

🎇We switched main url to new documentation! Old documentation would be available at for next two weeks. If you'll find some problems with new documentation and will need old docs available - contact us here or in telegram 🐱.

We'd like to invite Russian-speaking followers to our 100₂th online birthday party. Read more and register now: .And don't worry! We are planning to translate the recorded video into English and publish links later here. Stay tuned! 😸.

Good news, everyone! We've refactored CatBoost documentation and are inviting you to test it here: And from now on documentation sources in Yandex Flavored Markdown can be easily found in our repo.We are waiting for you PRs!😺.

#CatBoostPoll CatBoost already supports distributed training on Apache Spark and by separate processes from CLI. If you'd like CatBoost to support your favourite framework - please vote or reply with your variant😺.

RT @jhimmelreich: New paper: we tested how different ML methods perform on predicting administrative errors in US unemployment insurance da….

* Fixed CatBoost training on Windows & CUDA.* Fixed incorrect worker process termination in case of exception in main process.* And fixed some annoying bugs!😸.

🚀CatBoost 0.26.1 😺.News:.* R package: supported text features and virtual ensembles prediction.* New MultiRMSEWithMissingValues loss function that supports training multidimensional regression models with missing labels

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