Cross-validation: It is mainly used in modeling applications. In a given modeling sample, most of the samples are taken to build the model, and a small part of the sample is used for prediction with the newly established model, and the forecast error of this small sample
Purpose: The purpose of cross-validation is to get a reliable and stable model
K-fold cross-validation:
(1) First divide the data into K parts, use K-1 data for training, and the remaining one is used for verification.
(2) Perform the above operations in turn K times, so that the data for each training is different, and the data for each verification is also different.
(3) The results of K times are averaged, and
cross-validation can also be seen to see the effect. Used to adjust hyperparameters
