I have this time-series dataset that has 63 features, out of which 57 were manually engineered. While checking for collinearity, I get this correlation matrix: As can be seen there are a number of variables that are correlated/collinear. The ones that are deep red certainly need to be removed, but what about the ones on the bluer range? How do such variables (on the negative range of collinearity) effect regression models?
Also, I ran a recursive feature extraction process from
sklearn.feature_extraction module and it has recommended me 39 features to be the best (at default settings). Is RFE the best strategy while dealing with such features?