CatBoost was created in 2017 by Yandex, a Russian multinational IT company, to address the limitations of existing gradient boosting algorithms, especially in handling categorical features and reducing overfitting.
CatBoost
CatBoost is a gradient boosting library that uses categorical features efficiently and provides high-performance machine learning models for classification, regression, and ranking tasks.
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About CatBoost
Strengths of CatBoost include efficient handling of categorical features, excellent performance with default parameters, and reduced overfitting. Weaknesses include longer training times compared to some other algorithms and higher memory usage. Competitors include XGBoost, LightGBM, and Scikit-learn's Gradient Boosting Machine (GBM).
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How to hire a CatBoost expert
A CatBoost expert must have skills in Python or R programming, proficiency in handling and preprocessing categorical data, understanding of gradient boosting algorithms, experience with hyperparameter tuning, and knowledge of performance evaluation metrics for machine learning models.
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