Bootstrap Aggregating Trend 2026

Bootstrap Aggregating, or bagging, is an ensemble technique that improves model accuracy and stability by training multiple algorithms on random subsets of data. It reduces overfitting in high-variance models like decision trees. Data scientists and machine learning engineers benefit from bagging for robust predictions, commonly used in random forests for classification and regression tasks.