In each iteration, load5foldData is called to get 5 different datasets. Each dataset, consisting of the training set and the testing set, is used to train and test the model.
But those 5 datasets are generated from the same data. Thus, the data in fold0_x_test to test the model may be in fold1_x_train and it's used to train the model again, which means the model could learn from the fold$n$_x_test in each iteration. The whole data is used for training but there is no other separate testing set to evaluate the model, resulting in significantly higher accuracy.
In each iteration,
load5foldDatais called to get 5 different datasets. Each dataset, consisting of the training set and the testing set, is used to train and test the model. But those 5 datasets are generated from the same data. Thus, the data infold0_x_testto test the model may be infold1_x_trainand it's used to train the model again, which means the model could learn from thefold$n$_x_testin each iteration. The whole data is used for training but there is no other separate testing set to evaluate the model, resulting in significantly higher accuracy.