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K-fold cross validation :

K-fold cross validation : KWnE6

k-fold cross-validation is one of the most popular strategies widely used by data scientists. It is a data partitioning strategy so that you can effectively use your dataset to build a more generalized model. wherein the K-Fold Cross Validation the dataset is split into a K number of sections/folds where each fold is used as a testing set at some point. the current study took the scenario of 3-Fold cross-validation(K=3). Here, the data set is split into 3 folds. In the first iteration, the first fold is used to test the model and the rest are used to train the model. In the second iteration, the 2nd fold is used as the testing set while the rest serve as the training set. This process is repeated until each fold of the 3 folds have been used as the testing dataset (see Fig 9)

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k-fold
 cross-validation is one of the most popular strategies widely
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so
 that you can effectively
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use
 your dataset to build a more generalized model.

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see
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