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I have a dataset that has high collinearity among variables. When I created the linear regression model, I could not include more than five variables ( I eliminated the feature whenever VIF>5). But I need to have all the variables in the model and find their relative importance. Is there any way around it?. I was thinking about doing PCA and creating models on principal components. Does it help?.

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  • $\begingroup$ Why can’t you include more than five variables? $\endgroup$
    – Dave
    Oct 31 at 2:30
  • $\begingroup$ Because VIF increases beyond 5 when I use more than 5 features. $\endgroup$
    – Naseef
    Oct 31 at 17:50
  • $\begingroup$ So VIF exceeds $5$…how does that impact your analysis? $\endgroup$
    – Dave
    Oct 31 at 20:24
  • $\begingroup$ Doesn't it mean high collinearity in the data? So that I can't keep those features $\endgroup$
    – Naseef
    Nov 1 at 1:17
  • $\begingroup$ But VIF of 4.5 also means that there is (multi)collinearity. How does VIF $>5$ impact your analysis? $\endgroup$
    – Dave
    Nov 1 at 3:05
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PCA will generate „new“ (transformed) features which are orthogonal (non-correlated). However, since the original features are transformed, you can hardly claim to say a lot about the importance of (original) features based on PCA.

One obvious alternative would be to use a random forest (RF) to determine feature importance. Using tree based models (like RF or tree based boosting) you do not need to care about collinearity in the feature space.

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  • $\begingroup$ But my principal components are still a linear combination of the original variables, right?. Can I distribute the feature importance of the principal components to the original variables somehow? $\endgroup$
    – Naseef
    Oct 26 at 5:49
  • $\begingroup$ Not sure about this. Tend to say it is not a good idea… $\endgroup$
    – Peter
    Oct 26 at 19:18

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