Resampling
Resampling is a statistical method that involves drawing repeated samples from the original data to estimate the properties of the population. In data science, resampling is used to evaluate the performance of a machine learning model by repeatedly drawing samples from the training data and testing the model on each sample. This helps to estimate the model's performance on new, unseen data. Resampling techniques include cross-validation, bootstrapping, and permutation testing. Cross-validation involves dividing the data into multiple subsets and training the model on each subset while testing it on the remaining data. Bootstrapping involves drawing random samples with replacement from the original data to create new datasets for training and testing the model. Permutation testing involves randomly permuting the labels of the data to create new datasets for testing the model's performance.
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