Bagging classifier

A Bagging classifier is a machine learning technique that improves the accuracy of predictions by combining multiple models. It creates several versions of a model, each trained on a different random subset of the data, and then merges their predictions to make a final decision. This process reduces errors caused by overfitting, leading to more stable and reliable results. Think of it like consulting multiple experts and combining their opinions to make the best overall choice. Bagging is especially useful when individual models are prone to variability, helping to achieve better performance.