K Folds Algorithm

One round of cross-validation necessitates partitioning a sample of data into complementary subsets, performing the analysis on one subset (named the training set), and validating the analysis on the other subset (named the validation set or testing set).In summary, cross-validation combines (averages) measures of fitness in prediction to deduce a more accurate estimate of model prediction performance.

Steps and Performance

COMING SOON!