Bootstrapping in machine learning

Bootstrapping in machine learning is a statistical method that involves repeatedly sampling from a dataset with replacement to create many similar datasets. This technique helps estimate the variability or uncertainty of a model's predictions by training multiple models on these different samples. Think of it as making many slightly different "versions" of your data to see how consistent your model's results are. It's useful for assessing model stability, improving performance, and gauging confidence in predictions, especially when data is limited or when trying to prevent overfitting.