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Unlike conventional k fold cross-validation methods, successive training sets are supersets of those that come before them. It also adds all surplus data to the first training set that is always used to train the model. Advantages and Disadvantages of K fold Cross. There are some advantages of k fold cross-validation with over validation. 2019. 4. 15. · Some say k-fold cross validation is used on the training set of data to obtain an optimal model before applying the model to the testing set. This is model selection. You can choose the best model or hyper-parameter set via this procedure. But, note that, when you use your data for this purpose, i.e. exploited it to see if which configurations.

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