CatBoost is an open-source gradient boosting library 
with categorical features support

Get started

CatBoost Now Available in Open Source

July 18, 2017

Today, we are open-sourcing our gradient boosting library CatBoost. It is well-suited for training machine learning models on tasks where data is heterogeneous, i.e., is described by a variety of inputs, such as contents, historical statistics and outputs of other machine learning models. The new gradient-boosting algorithm is now available on GitHub under Apache License 2.0.

Developed by Yandex data scientists and engineers, it is the successor of the MatrixNet algorithm that is used within the company for a wide range of tasks, ranging from ranking search results and advertisements to weather forecasting, fraud detection, and recommendations. In contrast to MatrixNet, which uses only numeric data, CatBoost can work with non-numeric information, such as cloud types or state/province. It can use this information directly, without requiring conversion of categorical features into numbers, which may yield better results compared with other gradient-boosting algorithms and also saves time. The range of CatBoost applications includes a variety of spheres and industries, from banking and weather forecasting, to recommendation systems and steel manufacturing.

CatBoost supports Linux, Windows and macOS and can also be operated from a command line or via a user-friendly API for Python or R. In addition to open-sourcing our gradient-boosting algorithm, we are releasing our visualization tool CatBoost Viewer, which enables monitoring training processes in iPython Notebook or in a standalone mode. We are also equipping all CatBoost users with a tool for comparing results of popular gradient-boosting algorithms.

“Yandex has a long history in machine learning. We have the best experts in the field. By open-sourcing CatBoost, we are hoping that our contribution into machine learning will be appreciated by the expert community, who will help us to advance its further development,” says Name Surname, position at Yandex.

CatBoost has already been successfully tested in a variety of applications across a whole range of Yandex services, including weather forecasting for the Meteum technology, content ranking for the personal recommendations service Yandex Zen, and improving search results. Eventually, this algorithm will be rolled out to benefit the majority of Yandex services. Outside of Yandex, CatBoost is already being used by data scientists at the European Organization for Nuclear Research (CERN) to improve data processing performance in their Large Hadron Collider beauty experiment.

Large Hadron Collider particle identification

July 18, 2017

The Large Hadron Collider beauty (LHCb) experiment is one of the four major experiments running at the Large Hadron Collider (LHC), the world’s largest and most powerful particle accelerator, operating at the European Organization for Nuclear Research (CERN). In order to perform high-level physics measurements, scientists need to analyse data from particle collisions recorded at a rate of 40 million times per second.

These data represent “snapshots” of all the particles generated by collisions of LHC protons and flying through the volume of particle detectors placed around the proton-proton interaction region. In order to understand the entire picture of the underlying physics laws ruling the processes taking place in the collisions, it is extremely important to reconstruct the identity of each particle whose passage is recorded by the detectors. This is the main role of particle identification (PID) algorithms.


Fast, reliable and accurate PID algorithms are crucial to selecting interesting data. In almost all 400 or so papers published by the LHCb collaboration, it is evident that these aspects of PID algorithms play a crucial role in important discoveries.

To combine the information from the various subcomponents of the LHCb detector in an effort to achieve a more efficient PID performance, a team from the Yandex School of Data Analysis proposed the use of the new algorithm CatBoost. CatBoost is well suited to improve the accuracy of PID response because it works with different features types (including binary observables) and formats with state-of-the-art precision. The algorithm ideally meets LHCb requirements, working as a seamless complement with all inputs.

The algorithm was trained using simulated collisions resembling those taking place at the LHCb proton-proton interaction point. The algorithm uses about 60 input features describing the geometrical position of the detected particles, the aggregated detector response and the kinematic properties of the detected tracks.

After its implementation and deployment into LHCb codebase and event processing pipeline in June 2017, CatBoost’s best-in-class performance proved to improve accuracy without compromising efficiency. Initial tests show encouraging improvements in the identification of charged particles starting from the information that they release in the LHCb detector, with respect to other machine learning approaches available on the market. Ultimately, this new approach will lead to cleaner data to all particle physics experiments, making physicists’ work more efficient.

After seeing these initial positive results, the LHCb team is planning further experimentation with CatBoost in other LHCb projects.

CatBoost at ICML 2017

July 20, 2017

We will be happy to meet everyone at ICML in Sydney, Australia on August 6-11, 2017, where we will be showcasing CatBoost in the Yandex booth #16.

Our team will be there to showcase the usage and applications of our new gradient-boosting machine learning library. We’ll be happy to demonstrate training CatBoost on a variety of datasets, and go through the tricks CatBoost uses to work well on categorical features. You will learn how to access the CatBoost library from the command line, or via API for Python, sklearn, R or caret, and how to monitor training in iPython Notebook using our visualization tool CatBoost Viewer. We will also let you in on the secret of how to score well in a Kaggle contest!


We look forward to meeting you at our ICML stand in Sydney. Please drop by – we’ll even have some goodies to share!