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