Negatively correlated features Is it ok to use negatively correlated features in data modeling? Say I have features A and B that have a correlation coefficient of 0.2 and features C and D with -0.2 correlation coefficient, is it fine to use features C and D in the model, since they have a low negative correlation? Also, does this have different effects on a regression vs. classification problem?
There's no problem in having negative correlations, in fact, if you change C to -C the correlation will be positive and every machine learning model I know should be equivalent trained on a variable or the same one with the sign changed.
So no, in any supervised case, regression or classification, it doesn't matter if there are negative correlations among features, and it happens almost every time.