Bagging (bootstrap aggregating) is an ensemble ML technique that improves predictive model consistency by training multiple models on random subsets of training data and averaging their predictions. It reduces variance but not bias, contrasting with boosting, which addresses both. Bagging involves data splitting, parallel model training, and aggregating outputs. Common bagging models include random forests and bagged decision trees. Applications span classification, regression, and feature selection, especially in cases like customer churn prediction. Advantages include reduced variance, generalization to new data, and high parallelizability, while challenges involve increased computational needs, complexity, and hyperparameter tuning difficulties.
Building Robust AI Models With Bagging: Techniques, Benefits, and Applications
