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I'm new to machine learning and try to clarify my problem in research. I just wonder if I can compare the importance of two different variables in two different sorts. For example, A and B are two variables that I want to compare their contribution to ML accuracy. The rest of the variables (like C, D, and E) for each sort are the same.

If I got the results that the rest of the variables (C, D, and E) contributes more (obtain a higher importance score) in the model predicted by A and the rest, can I just say B performs better for ML and its contribution is more significant than the model with A and the rest?

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  • $\begingroup$ What kind of model do you plan to use? If you need to measure variable importance I recommend linear regression or decision trees. $\endgroup$
    – bstrain
    Dec 17 '19 at 9:29
  • $\begingroup$ Actually I have already got the variable importance list. Just wondering if I can compare the importance of two variables in two different sorts. Which one is more significant to accuracy. $\endgroup$
    – Ang Ji
    Dec 18 '19 at 10:32
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No you can not. You are forgeting about variable interactions (C-A,D-A,E-A, etc...) that could favor A.

You could answer following qestion: If I were to measure information in my variables via variability how would I proceede? For example PCA

The more information you have in the variable(predictor) the higher the chance that it will contribute more to accuracy

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  • $\begingroup$ OK, I'll check this later. So does it mean that I can just use statistic methods to identify which one contributes more to accuracy, instead of machine learning? $\endgroup$
    – Ang Ji
    Dec 18 '19 at 10:35
  • $\begingroup$ a) whats the difference b) Yes, you can just use statistic methods but not only. I gave you an example of how you can make reasonable comparisons c) Accept if this answer satisfies you $\endgroup$
    – Noah Weber
    Dec 18 '19 at 10:38
  • $\begingroup$ Correct. Those comparisons only hold when the models don’t change $\endgroup$
    – HEITZ
    Dec 19 '19 at 7:06

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