-
Categorical features and missing values in XGBoost: the native path vs one-hot
XGBoost can learn from missing numeric values and categorical columns without forcing every project through an impute-then-one-hot pipeline. The real decision is not whether native support exists; it
-
Tuning XGBoost without fooling yourself: early stopping, learning rate, and the parameters that matter
XGBoost tuning becomes dangerous when optimization and evaluation blur together. A workflow can try dozens of configurations, stop each run at its best validation round, select the lowest validation e
-
Gradient boosting from the ground up: why XGBoost and LightGBM still rule tabular data in 2026
Tabular ML has an unfashionable truth: the best model is often not the newest architecture.