howeverthe -value basically tells us that whatever the correlation coefficient iswe should not pay attention to it the following output in the screenshot illustrates the samein the first three cases that have high correlation coefficientswe would probably want to throw out either or since they seem to convey similar if not the same information in the last casehoweverwe should keep both features in our applicationthis decision would of course be driven by that -value although it worked nicely in the previous examplereality is seldom nice to us one big disadvantage of correlation-based feature selection is that it only detects linear relationships ( relationship that can be modeled by straight lineif we use correlation on non-linear datawe see the problem in the following examplewe have quadratic relationship