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I'm using Pearson and Spearman correlation between predicted value and ground truth to evaluate the performance of my model (a deep neural network). On my first dataset the longer I train my model the better Pearson and Spearman correlation are, but on my second dataset meanwhile Pearson increase, Spearman decrease. How is that possible? If the linear correlation (Pearson) increase, the non-linear correlation (Spearman) should be increasing too? I don't know how to interpret those results.

EDIT:
I have a vector with real scores and a vector with predicted scores, I just a calculate the correlation between both of them.

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  • $\begingroup$ What are you doing exactly ? Are you not computing correlations between predictors ? $\endgroup$ – Gabriel Romon May 16 '18 at 9:54
  • $\begingroup$ You could post this question on our stats website CV to see if you get any response. If you do, provide this link also in that post. $\endgroup$ – StubbornAtom May 17 '18 at 13:27

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