# PLS Predict Settings in SmartPLS

### Number of Folds

In k-fold cross-validation the algorithm splits the full dataset into k equally sized subsets of data. The algorithm then predicts each fold (hold-out sample) with the remaining k-1 subsets, which, in combination, become the training sample. *For example, when k equals 10 (i.e., 10-folds), a dataset of 200 observations will be split into 10 subsets with 20 observations per subset. The algorithm then predicts ten times each fold with the nine remaining subsets.*

### Number of Repetitions

The number of repetitions indicates how often PLS predict algorithm runs the k-fold cross validation on random splits of the full dataset into k folds.

Traditionally, cross-validation only uses one random split into k-folds. However, a single random split can make the predictions strongly dependent on this random assignment of data (observations) into the k-folds. Due to the random partition of data, executions of the algorithm at different points of time may vary in their predictive performance results (e.g., RMSE, MAPE, etc.).

Repeating the k-fold cross-validation with different random data partitions and computing the average across the repetitions ensures a more stable estimate of the predictive performance of the PLS path model.

Sumber : Discover PLS