Explain the mechanism of n-fold crossvalidation

Cross-validation involves partitioning the data into subsets or ‘folds.’ There are a variety of ways in which this partitioning can be done, but the most common is by n-fold cross-validation. This involves splitting the data into n equal sized (or similarly sized) subsets. For each fold, one of the subsets is selected as the training set, and the other n – 1 subsets are used as testing sets. The model is then run on the training set and evaluated on the testing sets. This process is repeated for each fold and the results are then combined to get an overall estimate of the model’s performance. N-fold cross-validation can help reduce the variance in the evaluation of a model, as it uses all the data for both training and testing.