*2.6. K-fold Cross Validation*

Generally, to evaluate the performance of a model, the dataset is randomly split into two subsets for training and testing according to a certain ratio. Test set obtained through this method may be unreliable to estimate the real performance of the model, especially when the size of the dataset is relatively small. K-fold cross validation utilizes all data to test the model, and thus could better estimate the generalization ability of the model. The fold number K is usually set to 5 or 10 [30,31]. In this paper, K was set to 5 as a trade-off between the bias of the result and time consumption for training. The leave-one-out method, a special case of K-fold cross validation, was utilized. In this case, the number of folds equals the number of instances.
