Cross-validation is a technique used in machine learning to evaluate the performance of a statistical model, and to estimate the accuracy of the model on new data sets that have not been used to train the model.
Cross-validation is performed by splitting the dataset into a training set and a validation set. The model is trained with the training set and evaluated with the validation set. This process is repeated several times, with different divisions of the data into training and validation sets. At the end, the results of the different evaluations are averaged to obtain a more accurate measure of the model's performance.
Cross-validation is a useful technique to avoid overfitting in the model, as it allows to assess its generalisability. The technique can also be useful in model selection and optimisation of model parameters.
There are several types of cross-validation, including k-fold cross-validation, leave-one-out cross-validation, and stratified cross-validation. Each type has its own characteristics and may be more suitable for certain applications.
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