Cross Validation. Cross-validation is a technique for…

Cross-validation is a technique for evaluating a machine learning model and testing its performance. Cross-validation is a technique used to evaluate the performance of a machine learning model by training it on different subsets of the data and testing it on the remaining subset. Cross-validation is also known as rotation estimation or out-of-sample testing. Rotation estimation refers to the process of rotating, or splitting, the data into different subsets. Simply put, in the process of cross-validation, the original data sample is randomly divided into several subsets.