How Can RMSECV Values Be Accessed and Interpreted After PLS-DA/OPLS-DA Model Construction?
Upon completing PLS-DA (Partial Least Squares Discriminant Analysis) or OPLS-DA (Orthogonal Partial Least Squares Discriminant Analysis) modeling and generating the corresponding 2D plots, the model’s performance can be assessed using specialized software platforms such as SIMCA or MetaboAnalyst, among others. In SIMCA, the Root Mean Square Error of Cross-Validation (RMSECV) can be located in the "Results" panel on the left-hand side of the interface. This metric quantifies the average predictive error observed during the cross-validation procedure. In general, a lower RMSECV value reflects better predictive performance of the model.
It is important to note, however, that RMSECV assesses only the predictive error component of model performance. It should not be used in isolation for model evaluation. Comprehensive assessment should also include additional aspects such as model interpretability, the risk of overfitting, and generalization ability as demonstrated by performance on independent test datasets.
MtoZ Biolabs, an integrated chromatography and mass spectrometry (MS) services provider.
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