Bootstrapping
Bootstrapping is a statistical technique that involves resampling a dataset with replacement to estimate the sampling distribution of an estimator. In other words, it is a method of generating new data samples from an existing dataset by randomly sampling observations with replacement. This technique is commonly used in machine learning and data science to estimate the accuracy of a model or to calculate confidence intervals for a statistic. Bootstrapping can be particularly useful when the underlying distribution of the data is unknown or when the sample size is small. By generating multiple bootstrap samples and calculating the statistic of interest for each sample, we can obtain an empirical estimate of the sampling distribution and use it to make inferences about the population.
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