although the human eye immediately sees the relationship between and in all but the bottom-right graphthe correlation coefficient does not it is obvious that correlation is useful to detect linear relationshipsbut fails for everything else for non-linear relationshipsmutual information comes to the rescue mutual information when looking at the feature selectionwe should not focus on the type of relationship as we did in the previous section (linear relationshipsinsteadwe should think in terms of how much information one feature providesgiven that we already have another