Bootstrap aggregating

Bootstrap aggregating, or "bagging," is a machine learning technique that improves prediction accuracy by combining multiple models. It works by taking many random samples from the original dataset, training a separate model on each sample, and then combining their predictions—often by averaging or voting. This process reduces errors caused by overfitting or variability in individual models, resulting in more stable and reliable results. Think of it as consulting multiple experts, each with slightly different viewpoints, and then making a final decision based on their collective input.