Resampling algorithm

Resampling is a statistical method used to improve the accuracy of data analysis by repeatedly drawing samples from a dataset. It involves techniques like bootstrapping, where multiple new datasets are created by randomly selecting data points with replacement, and cross-validation, where data is split into parts for training and testing. These methods help estimate the stability, variability, or performance of models without needing extra data. Essentially, resampling provides a way to make more reliable inferences and assessments from existing data by simulating numerous variations.