#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. https://t.co/anEnCE2WWi

#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.

Gradient boosting methods have been proven to be an important strategy. This article with @neptune_ai aims to investigate and compare the efficiency of three gradient methods focusing primarily on @CatBoostML.

#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. https://t.co/1GuTwIBMBZ

See full release notes in our github https://t.co/ZzKsvI8NA0 😺🥳

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 https://t.co/O9evuZN38I 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 https://t.co/z04Av5Jaw8 🐱

We'd like to invite Russian-speaking followers to our 100₂th online birthday party. Read more and register now: https://t.co/fYpVzxsYA2 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: https://t.co/5KQlZMvMc4 And from now on documentation sources in Yandex Flavored Markdown can be easily found in our repo https://t.co/tQb3KajMkH 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😺

New paper: we tested how different ML methods perform on predicting administrative errors in US unemployment insurance data. Turns out: @CatBoostML is more accurate, along several measures, than every deep learning model tested. (open access for two weeks) https://t.co/Uv1Xdb4QjQ

* 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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