An optimized distributed gradient boosting library designed to be highly efficient, flexible, and portable.
XGBoost
- Pitón
- IA y aprendizaje automático, Bioinformática, Análisis de datos
- Colaboración multifuncional, Aprendizaje automático, Algoritmos de mantenimiento predictivo, Control de calidad, Gestión de calidad, Análisis estadístico, Control estadístico de procesos (CEP)
Características:
- Gradient boosting algorithms (GBDT,DART,GOSS),regularization techniques (L1,L2) to prevent overfitting,handles missing values automatically,parallel and distributed computing for speed,cross-validation tools,tree pruning,early stopping,supports various objective functions and evaluation metrics,can be used for classification,regression,ranking
Precios:
- Gratis
- High performance and speed, excellent accuracy, robust to overfitting with proper tuning, feature importance ranking, widely used in machine learning competitions and industry, supports various languages including Python.
- Can be sensitive to hyperparameters (requires careful tuning), can be prone to overfitting on small or noisy datasets if not regularized properly, less out-of-the-box support for categorical features compared to LightGBM or CatBoost (requires preprocessing).
Ideal para:
- Data scientists and machine learning engineers seeking a high-performance, accurate, and scalable gradient boosting framework for a wide range of supervised learning tasks.