What is CV in cross validation?

Cross validation (CV) is one of the technique used to test the effectiveness of a machine learning models, it is also a re-sampling procedure used to evaluate a model if we have a limited data.

What is the purpose of performing cross validation?

The goal of cross-validation is to test the model’s ability to predict new data that was not used in estimating it, in order to flag problems like overfitting or selection bias and to give an insight on how the model will generalize to an independent dataset (i.e., an unknown dataset, for instance from a real problem).

Which statement is true about k fold cross validation?

22) Which of the following options is/are true for K-fold cross-validation? Increase in K will result in higher time required to cross validate the result. Higher values of K will result in higher confidence on the cross-validation result as compared to lower value of K.

What is repeated k fold cross validation?

Repeated k-fold cross-validation provides a way to improve the estimated performance of a machine learning model. This involves simply repeating the cross-validation procedure multiple times and reporting the mean result across all folds from all runs.