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) dl.acm.org/doi/10.1145/34…

#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😺

Good news, everyone! We've refactored CatBoost documentation and are inviting you to test it here: catboost.ai/en/docs-beta/ And from now on documentation sources in Yandex Flavored Markdown can be easily found in our repo github.com/catboost/catbo… We are waiting for you PRs!😺

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

🎇We switched main url to new documentation! Old documentation would be available at catboost.ai/docs-old/ 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 t.me/catboost_en 🐱

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. youtube.com/watch?v=lhaOYw…

#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! 😺 youtu.be/ce1VULptNWQ

#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 youtu.be/fpUEoy60x24

#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.🚀 youtu.be/iuRGv31mcuI

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!😺

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. bit.ly/3fGFSjS

#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 youtu.be/RNT1o2gu5Ms

#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. youtu.be/m3E35snIrAM

#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. youtu.be/O2OJ_JWYV0I