What are the different types of cross validation?

Cross-validation is a technique used in machine learning to assess the performance of a model on unseen data. There are different types of cross-validation methods that can be employed depending on the specific needs of the analysis. The most common type is k-fold cross-validation, where the dataset is split into k equal-sized folds and the model is trained and evaluated k times, each time using a different fold as the validation set. Another method is stratified k-fold cross-validation, which ensures that the distribution of target classes in each fold is representative of the overall dataset. Leave-one-out cross-validation is a variation where each individual sample acts as the validation set, and this process is repeated for all samples. Time series cross-validation is suitable for sequential data, where the data is split into sequential blocks to preserve the temporal order while evaluating the model's performance. The choice of cross-validation method depends on factors such as dataset size, balance, and the presence or absence of temporal dependence.

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