Description
Gradient boosting decision trees (GBDT) is a powerful machine-learning technique known for its high predictive power with heterogeneous data. In this talk, we will explore scikit-learn's implementation of histogram-based GBDT called HistGradientBoostingClassifier/Regressor and how it compares to other GBDT libraries such as XGBoost, CatBoost, and LightGBM.