6.2 K-Cross folds Technique
The k- cross fold technique is widely used to validate the performance of data mining models as well as statistical analysis of datasets [34-35]. In k- cross fold technique, k is defined as the number of folds in the dataset in which k-1 folds is used as training instances and kth fold is used as test instance. In this paper, the numbers of folds are 10 i.e. the dataset is divided into 10 parts such that\(D=\{a_{1},\ a_{2},\ a_{3}\ldots\ldots.\ a_{10}\}\). However, 9 folds out of ten folds are used as training instances and the 10th fold is used as test instance that gives the accuracy of the model. In the k- cross folds technique the test instance is used to predict the class labels. A systematic diagram of 10- cross fold technique is given in Figure 4.
Figure 5: The systematic diagram of 10- cross fold technique in which bold red alphabet portion of the wheel act as a test instance while others act as training instances. This process will be executed up to 10 iterations and the each iteration will be consists the different test instance