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Practical Python guides for training, tuning and deploying XGBoost models. 7 articles.

XGBoost for Classification · Binary, Multi-Class, and Ranking

How XGBoost handles classification problems — the objective functions, probability calibration, class imbalance strategies, and multi-class extensions.

XGBoost Distributed Training · Multi-Node and Dask Integration

How to train XGBoost across multiple machines — the distributed framework, Dask integration, Spark integration, and practical deployment patterns.

XGBoost for Regression · Objective Functions and Metrics

XGBoost regression guide — squared error, absolute error, pseudo-Huber, Tweedie, Gamma, and quantile regression objectives and when to use each.

XGBoost Cross-Validation · cv() vs Manual K-Fold

How XGBoost's built-in cross-validation works — the cv() function, stratified folds, and how to use it for both hyperparameter tuning and final model evaluation.

XGBoost Custom Objective Functions · Beyond Built-in Losses

How to implement custom objective functions and evaluation metrics in XGBoost — gradient and hessian derivation, worked examples for quantile loss and Huber loss.

XGBoost Hyperparameter Tuning · A Systematic Guide

How to tune XGBoost hyperparameters effectively — the ordering of parameters, the most impactful knobs, and practical search strategies from grid search to Bayesian optimization.

XGBoost Hyperparameters · A Practical Tuning Guide

A field guide to XGBoost's most important hyperparameters — what each one does, how they interact, and a recommended tuning sequence.