The analysis serves as a diagnostic for possible overfitting of the PLS path model to the training data.īased on the procedures suggested by Shmueli et al. They allow generating different out-of-sample and in-sample predictions (e.g., case-wise and average predictions), which facilitate the evaluation of the predictive performance when analyzing new data (that was not used to estimate the PLS path model). These procedures are combined in the PLSpredict package for the statistical software R. (2016) proposes a set of procedures for prediction with PLS path models and the evaluation of their predictive performance. The method uses training and holdout samples to generate and evaluate predictions from PLS path model estimations. The PLSpredict algorithm has been developed by Shmueli et al.
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