XGBoost

XGBoost (eXtreme Gradient Boosting) is a machine learning algorithm based on gradient-boosted decision trees. While LLMs and deep learning dominate headlines, XGBoost remains arguably the most important algorithm for structured, tabular data—the kind of data that most businesses actually run on.

The algorithm works by building an ensemble of decision trees sequentially, where each new tree focuses on correcting the errors of the previous ones. This "boosting" approach, combined with XGBoost's aggressive optimization (regularization, tree pruning, handling of missing values, parallel computation), produces models that are fast to train, highly accurate, and remarkably robust. It was created by Tianqi Chen in 2014 and quickly became the dominant algorithm for tabular prediction tasks.

XGBoost's track record is striking. It has won more Kaggle competitions than any other single algorithm. In 2025, despite the deep learning revolution, XGBoost and its variants (LightGBM, CatBoost) still outperform neural networks on most tabular data benchmarks. For predicting customer churn, credit risk, fraud detection, recommendation ranking, and countless business applications built on structured databases, gradient-boosted trees remain the tool of choice.

This creates an interesting dynamic in the AI landscape. While attention focuses on frontier foundation models and their trillion-parameter architectures, a huge fraction of production AI systems—the ones actually driving the 6% EBIT impact that leading organizations report—run on XGBoost or similar ensemble methods. The lesson: the right tool depends on the data. Unstructured data (text, images, audio) favors deep learning. Structured data often favors gradient-boosted trees. Both are essential components of the AI toolkit.