OLS optimal estimator criteria

KOLS (k-fold oversampling with least squares) is an optimal estimator criteria that helps to reduce the bias in estimates while providing the best balance between generalization and accuracy. KOLS uses an iterative process, known as the k-fold cross-validation, to evenly divide a dataset into different folds or subsets and then uses the least squares method of estimation to determine the optimal model. The best model is chosen based on its accuracy and its ability to generalize well to other data sets. KOLS also helps reduce over fitting by randomly splitting the data into multiple subsets and then running different training algorithms on each subset. This helps identify patterns that may be missed when using a single training and test set.