Browse all content tagged with Ensemble Learning
Bagging, short for Bootstrap Aggregating, is a fundamental ensemble learning technique in AI and machine learning that improves model accuracy and robustness by training multiple base models on bootstrapped data subsets and aggregating their predictions.
Gradient Boosting is a powerful machine learning ensemble technique for regression and classification. It builds models sequentially, typically with decision trees, to optimize predictions, improve accuracy, and prevent overfitting. Widely used in data science competitions and business solutions.
XGBoost stands for Extreme Gradient Boosting. It is an optimized distributed gradient boosting library designed for efficient and scalable training of machine learning models, known for its speed, performance, and robust regularization.
