Struggling with missing data? XGBoost simplifies the process by handling it internally using its sparsity-aware split finding algorithm. Learn how it finds the optimal "default direction" for missing values at every tree split by testing which path maximizes information gain. This allows you to train robust models directly on incomplete datasets without manual imputation.

XGBoost is one of the fastest gradient boosting algorithms, designed for high-dimensional and large-scale datasets. This guide explains its core optimizations—including approximate split finding, quantile sketches, and weighted quantile sketches—that reduce computation time while maintaining high accuracy.

XGBoost is one of the most powerful tools for structured/tabular data — known for its speed, scalability, and high performance. In this post, I’ve shared a detailed explanation of what makes XGBoost so effective, along with its history, features, and real-world use. A great resource for anyone learning ML!