CatBoost is a high-performance open source library for gradient boosting on decision trees
Training with CatBoost on a CPU/GPU cluster with a dataset of trillion requests helps to identify malicious bot traffic.
Careem, the leading ride-hailing platform for the greater Middle East, explained how CatBoost helps predicting their customer next move.
Gradient boosting benefits from training on huge datasets. In addition, the technique is efficiently accelerated using GPUs. Read details in this post.
On December 2018, on NeurIPS conference in Montreal, Yandex team presented two papers related to CatBoost, an open-source machine learning library developed by Yandex.
CatBoost team continues to make a lot of improvements and speedups. What new and interesting have we added in our two latest releases and why is it worth to try CatBoost now? We'll discuss it in this post.
New superb tool for exploring feature importance, new algorithm for finding most influential training samples, possibility to save your model as cpp or python code and more. Check CatBoost v0.8 details inside!