Master Bagging in Machine Learning

Bootstrap Aggregating, or bagging, is an ensemble meta-algorithm that enhances the stability and accuracy of machine learning algorithms. It reduces variance and helps prevent overfitting, especially in decision tree models. Bagging works by training multiple classifiers on different bootstrap samples and combining their predictions. Each classifier votes, and the class with the majority vote is selected. This technique improves model performance by averaging predictions from various models.

For more details, please read the GeeksforGeeks article: Bagging vs Boosting in Machine Learning.