🪐 2-Gradient Boosting Machines and Low-Default Modeling: A repository for research, implementation, and best practices with Gradient Boosting methods (GBM, XGBoost, LightGBM), H2O AutoML, and robust strategies for modeling extreme class imbalance ("Low Default") in data science for finance and risk.
natural-language-processing h2o randomforest xgboost gbm lightgbm machinelearning imbalanced-data gradientboosting smote fraud-detection anomaly-detection credit-risk disease-prediction risk-analytics model-interpretability oneness-consciousness auto-machine-learning low-default-modeling financia-lmodeling
-
Updated
Oct 17, 2025 - Jupyter Notebook