Hi everyone,
I am reading in the literature about how to interpret MSEP/RMSEP/R2/Q2 for my sPLS models and I am at a loss with the maths! I understand that lower MSEP/RMSEP indicate a better predictive accuracy, a higher R2 suggests a better fit to the training data and a higher Q2 suggests a better predictive ability. However, what does getting an MSEP of 1.04 ± 0.23 for example actually mean?
Also, as I only have 10 samples, my Q2 starts close to 0 or even negative, and adding a 2nd component always makes it smaller/more negative. Does this mean I am (already) overfitting my data?
Thank you in advance!
Best wishes,
Evelyn