Resampling Methods
Resampling methods are a class of statistical techniques that involve repeatedly drawing samples from a dataset in order to estimate a population parameter or to validate a model. These methods are particularly useful when the dataset is small or when the underlying distribution is unknown. The most common resampling methods are bootstrapping and cross-validation. Bootstrapping involves randomly sampling the dataset with replacement to create multiple new datasets, from which estimates of the population parameter can be obtained. Cross-validation involves partitioning the dataset into training and testing sets, and then repeatedly fitting the model on the training set and evaluating its performance on the testing set. Resampling methods can be used for a variety of tasks, including model selection, hyperparameter tuning, and assessing the stability of model estimates.
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