Which Parameters Should Be Adjusted When the Q² Value in PLS-DA/OPLS-DA 2D Plots Is Negative?
A negative Q² value in PLS-DA (Partial Least Squares Discriminant Analysis) or OPLS-DA (Orthogonal Partial Least Squares Discriminant Analysis) typically indicates poor predictive performance. The Q² metric, derived from cross-validation, is employed to assess the model's ability to predict new data. A negative Q² implies that the model performs worse than a null or baseline model, suggesting significant issues with model generalizability. To enhance the predictive performance, the following parameters may be considered for adjustment:
Adjusting the Number of Components
Modify the number of components (latent variables) used in the PLS-DA or OPLS-DA model. An inappropriate number of components may lead to overfitting (too many) or underfitting (too few), both of which can degrade model performance.
Data Preprocessing Optimization
Apply suitable preprocessing techniques to the dataset. These may include standardization, normalization, log transformation, and mean-centering. Proper preprocessing is essential for stabilizing variance and improving model predictability.
Variable and Feature Selection
Eliminate variables that are irrelevant, noisy, or redundant. Employ robust feature selection strategies, such as evaluating Variable Importance in Projection (VIP) scores, to retain those features that contribute significantly to model discrimination.
Cross-Validation Strategy
Refine the cross-validation approach or its parameters. Techniques such as leave-one-out cross-validation (LOOCV) or K-fold cross-validation can be tested, and the fold number (K) adjusted to assess model robustness.
By carefully tuning these parameters, both the predictive capacity and stability of the model can be substantially improved. In practical applications, this process often involves iterative testing and comparative evaluation under different parameter configurations to determine the most effective modeling strategy.
MtoZ Biolabs, an integrated chromatography and mass spectrometry (MS) services provider.
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