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What Is CatBoost? (Definition, How Does It Work?) | Built In

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CatBoost is an open-source gradient boosting library that builds decision trees optimized for categorical data, reducing overfitting and improving accuracy in classification, regression and similar machine learning tasks.

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Kirill Eremenko joins Jon Krohn for another exclusive, in-depth teaser for a new course just released on the SuperDataScience platform, “Machine Learning Level 2”. Kirill walks us through why decision trees and random forests are fruitful for businesses, and he offers hands-on walkthroughs for the three leading gradient-boosting algorithms today: XGBoost, LightGBM, and CatBoost. Watch the full episode here: https://bit.ly/sds771 Thank you to Ready Tensor and Data Universe, for supporting this ep

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