xgboost

Developed by Tianqi Chen, it brought computational efficiency improvements (parallel tree construction, cache-aware access patterns, out-of-core computation for large datasets) that made gradient boosting practical at the scale required for industry applications.

The algorithm builds an ensemble of decision trees sequentially, with each tree learning to correct the errors of its predecessorsa process optimized through a second-order Taylor approximation of the loss function that enables more principled regularization than classical gradient boosting.

XGBoost supports L1 and L2 regularization natively, handles missing values without preprocessing, and provides feature importance scores that help practitioners understand which input variables drive predictions.

It integrates with Scikit-learn, Spark, Dask, and GPU acceleration through CUDA for datasets too large for CPU-bound training.

Data scientists working on structured/tabular prediction problemscredit scoring, fraud detection, demand forecasting, customer churn prediction, ad click-through rate estimationreach for XGBoost as a high-performing baseline that often matches or exceeds more complex models on tabular data.

Its interpretability relative to deep learning, training speed, and strong out-of-the-box performance with minimal hyperparameter tuning make it the default algorithm for tabular ML competitions and many production applications where neural networks would require significantly more data and engineering effort to deliver comparable results.