Statements (55)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:machine_learning
|
| gptkbp:application |
gptkb:scoring_system
gptkb:Biology regression |
| gptkbp:community_support |
active community
|
| gptkbp:developed_by |
gptkb:Yandex
|
| gptkbp:feature |
cross-validation
GPU support automatic handling of categorical variables model interpretation tools ordered boosting symmetric tree structure |
| gptkbp:has_documentation |
available online
|
| https://www.w3.org/2000/01/rdf-schema#label |
Cat Boost
|
| gptkbp:is_compatible_with |
gptkb:Java
gptkb:Python gptkb:R gptkb:Scala |
| gptkbp:language |
gptkb:C++
|
| gptkbp:performance |
competitive with Light GBM
competitive with XGBoost |
| gptkbp:release_date |
gptkb:2017
|
| gptkbp:supports |
categorical features
|
| gptkbp:use_case |
gptkb:sports_team
gptkb:recommendations financial modeling natural language processing real estate valuation supply chain optimization traffic prediction customer segmentation credit scoring fraud detection image classification recommendation systems sales forecasting time series forecasting healthcare analytics anomaly detection insurance risk assessment Kaggle competitions social media analysis marketing analytics market basket analysis telecommunications analysis customer churn prediction energy consumption prediction e-commerce analytics employee attrition prediction |
| gptkbp:uses |
gradient boosting
|
| gptkbp:bfsParent |
gptkb:Gradient_Boosting_Machines
gptkb:DGX_A100 gptkb:Light_GBM gptkb:Gradient_Boosted_Trees |
| gptkbp:bfsLayer |
6
|