bagging

Bagging, short for bootstrap aggregating, is a machine learning technique used to improve the accuracy of models. It works by creating multiple subsets of data from the original dataset through random sampling, allowing some data points to be repeated while others may be left out. Each subset is used to train a separate model. The final prediction is made by averaging their outputs (for regression) or taking a majority vote (for classification). This process reduces variance and helps prevent overfitting, leading to more robust and reliable predictions in various applications.