7 XGBoost Tricks for More Accurate Predictive Models - KDnuggets
KDnuggets
by Iván Palomares CarrascosaFebruary 20, 2026
AI-Generated Deep Dive Summary
Ensemble methods like XGBoost (Extreme Gradient Boosting) are widely used for building accurate predictive models by combining multiple weaker estimators into a powerful one. This article highlights seven Python tricks to enhance the performance of XGBoost, particularly when using its standalone library. By leveraging these techniques, readers can improve model accuracy and efficiency, especially on structured data like tabular datasets.
One key approach is adjusting hyperparameters such as learning rate and the number of estimators. Reducing the learning rate while increasing the number of trees often boosts accuracy, as it allows the model to learn more gradually. Additionally,
Verticals
aidata-science
Originally published on KDnuggets on 2/20/2026