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What is the meaning of increasing R-squared and Q-squared values with increasing number of PLS factors?

R-squared (R‍2) will always increase as you add more PLS factors because it measures the strength of the least-squares fit to the training set activities. More precisely, an R-squared value of 0.9 means that the model accounts for 90% of the variance in the observed activities for the training set. The value gets closer and closer to 1 (i.e., 100%) as more PLS factors are incorporated into the fit.

Q-squared (Q‍2) is the R-squared value that you get from applying the QSAR model to the test set instead of the training set. Since the model is not directly calibrated to fit the test set, Q-squared may or may not increase as you add more PLS factors. But if it's a good model (i.e., it embodies the SAR and you've picked a reasonable number of PLS factors), Q-squared will be comparable in value to R-squared.

Keywords: Phase, Strike

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