K-Fold Cross-Validation:
K-fold Cross-validation is a commonly employed method for assessing the test error of a predictive model. The basic concept involves randomly dividing the dataset into K equal-sized segments or folds. In each iteration, one of these segments (referred to as part K) is left out, while the model is trained using the remaining K-1 segments. Predictions are then generated using the omitted Kth segment. This process is repeated for each segment, with K taking values from 1 to K. The outcomes are then combined. As each training set is just (K-1)/K the size of the original training set, it often results in an upward bias in prediction error estimates. Although this estimate’s variance can be substantial, it is minimized when K is equal to the total number of data points (K = n).
Distinguishing Between Test and Training Errors:
Test error represents the typical error that arises when utilizing a statistical learning technique to forecast outcomes for fresh observations that were not part of the model’s training. Conversely, training error can be effortlessly computed by employing the same technique on the data that was utilized during the model’s training phase. It is important to recognize that the training error rate frequently diverges considerably from the test error rate, with the former often giving rise to an underestimated estimate of the latter.