Why Is Q2 Negative in the PLS-DA/OPLS-DA 2D Plot?
The Q2 value is a statistical metric used in PLS-DA/OPLS-DA to assess the model's predictive capability. Specifically, Q2 represents the squared correlation coefficient for prediction. During modeling, Q2 is obtained through cross-validation. A positive Q2 indicates that the model has some predictive capability for the test dataset, whereas a negative Q2 usually signifies poor predictive performance or overfitting of the model.
Several factors may contribute to a negative Q2 value, including:
1. Model Overfitting
A model that is too complex may perform well on the training data but fail to generalize to the cross-validation dataset.
2. High Levels of Noise in the Data
If the data contains significant noise or weak associations between variables, the model may struggle to detect meaningful patterns.
3. Insufficient Sample Size
A small number of training samples may prevent the model from accurately capturing the variability present in the data.
4. Inappropriate Selection of Model Parameters
For example, using an incorrect number of components may reduce the model's predictive ability.
5. Data Preprocessing Issues
Improper handling of missing values, normalization, or standardization steps may lead to suboptimal model performance.
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