Yandex Upgrades Open-source Machine Learning Library CatBoost

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Yandex, a technology company that builds intelligent products and services powered by machine learning, announced that CatBoost 1.0.0, a major version of their open-source machine learning library. The new version goes far beyond a run-of-the-mill upgrade and is the culmination of four years of work by the Yandex Team.  

Since its initial launch in 2017, tech innovators have used CatBoost for everything from streaming service user recommendations to particle classification to destination prediction for Careem, a ride-haling service. The library is based on gradient boosting, a form of machine learning that analyzes a wide range of data inputs by progressively training more complex models to maximize the accuracy of predictions. 

The major version of CatBoost fixes all bugs and includes a number of improvements, including:  

  • Spark support for distributed learning 
  • New, improved and convenient documentation with open code 
  • Speed boost for CPU and GPU learning and overall faster model training (binary classification on CPU is 15-35 % faster) 
  • Predictive, multiple label classification  

“Far beyond a run-of-the-mill system update, CatBoost 1.0.0 is the transformation from an open-source machine learning library to a stand-alone, ready-to-use product,” commented Stanislav Kirillov, head of Yandex ML Systems Group. “We are proud of the incredible, diligent work our team has put into reaching this milestone and are committed to continuing to improve and innovate CatBoost.”  

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