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Description
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Feature Description
CatBoost is a gradient boosting algorithm that handles categorical features naturally without requiring extensive preprocessing. It uses ordered boosting to prevent target leakage and provides robust and accurate predictions for both classification and regression tasks.
Use Case
Integrating CatBoost would enhance the project's capability to work with complex datasets containing many categorical variables, such as in customer segmentation or fraud detection. Its ability to manage categorical data directly would streamline the modeling process, resulting in faster development cycles and more accurate predictive models
Benefits
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Priority
High
Record
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