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My data contains binary outcome variable and 4 regressors. 2 regressors are binary and 2 are continuous (truncated to whole numbers).

  • Under what circumstances should the continuous variables be standardised in logistic regression?

According to this article variables should be standardised if they are not in the same scale. However, this is slightly confusing for me, we have not talked about standardisation in the context of logistic regression in class.

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    $\begingroup$ I can't see a reason to ever standardize them. $\endgroup$ Commented Apr 22, 2018 at 21:23
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    $\begingroup$ There is no requirement to standardized predictors for logistic regression, and it is rare that I see this done. $\endgroup$
    – Bryan
    Commented Apr 22, 2018 at 21:26
  • $\begingroup$ You don't need to do that. $\endgroup$
    – SmallChess
    Commented Apr 23, 2018 at 0:53

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You don't need to standardize for normal logistic regression as long as one keeps units in mind when interpreting the coefficients.

Standardizing can help with interpreting feature importance because then the coefficients should be apples to apples. (ie if your two standardized continuous variables have coeff of 0.01 and 0.7 then you know that the 2nd one is much more important.)

However for regularized logistic regression continuous variables should be standardized for best results.

Is standardisation before Lasso really necessary?

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    $\begingroup$ You can also estimate importance without standardizing using likelihood ratio chi-square statistics. $\endgroup$ Commented Apr 23, 2018 at 12:13

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