CatBoost is an open-source machine learning library written in Python, used for both regression and classification purposes. It is based on gradient boosting and is a part of the Yandex Machine Learning platform. CatBoost was developed by the Yandex team of researchers and engineers, and was released in 2017.
The main purpose of CatBoost is to easily create models that integrate with pre-existing applications and data sourced from different types of databases. The library offers different types of standard algorithms, such as gradient boosting, Random Forest, and Logistic Regression, as well as additional options for input-output data processing. In particular, CatBoost is designed to quickly and accurately build predictive models for structured data, and is optimised to work with categorical fields, making it particularly beneficial for categorical data applications.
CatBoost can perform many powerful operations on structured data, such as feature engineering, feature selection, iterative imputation, and automatic parameter tuning. This allows users to create complex architectures using different algorithms without having to manually tune each parameter. Furthermore, the library has advanced algorithms and techniques such as one-hot encoding, permutation matrices, and the ability to work with different input types.
CatBoost also has powerful tools for feature selection and parameter tuning. These tools allow users to optimize their models efficiently and accurately. Additionally, the models can be visualized using a variety of techniques such as partial dependence plots, SHAP values, and feature importance views.
Overall, CatBoost is an efficient, powerful and user-friendly machine learning library which can be used to solve a variety of tasks, from prediction to feature engineering. This versatile library can be beneficial to applications dealing with structured data and categorical data, and is sure to become increasingly important for data practitioners as time progresses.
