1
$\begingroup$

How can I convert predicted probabilities of a logit model into predicted binary response ? Can I consider 0.5 as cut point to convert probabilities to binary variable (0,1). Or should I use binomial distribution to generate binary variable where predicted probabilities are used as success probability. By the way I am doing it for cross validation of the fitted model.

$\endgroup$
4
  • 1
    $\begingroup$ Using 0.5 is a good "default". However, if your two misclassifications have different costs or implications, another number might be better. What is the context? $\endgroup$
    – Peter Flom
    Dec 18, 2013 at 12:49
  • $\begingroup$ what is cost? how can i define it? context labor force participation \... $\endgroup$
    – Mazumder
    Dec 18, 2013 at 15:12
  • $\begingroup$ Cost is how bad each type of misclassification is. You would have to figure it out on substantive basis. $\endgroup$
    – Peter Flom
    Dec 18, 2013 at 15:33
  • $\begingroup$ this is an overly complex introduction to this topic, but should give you the basics to produce cost/utility function if you want: ncbi.nlm.nih.gov/pubmed/17099194 $\endgroup$
    – charles
    Dec 19, 2013 at 0:02

1 Answer 1

2
$\begingroup$

usually it is feasible to iterate over predicted probabilities with various cut-off points from 0 to 1 with an increment of, say 0.01, and to construct some metric that is of interest to you (i.e. which you want to maximize). Be it accuracy, sensitivity, specificity, K-S score or value of other variable that may be not part of your model.

Then plot the cutoff VS that variable and you will have an idea which cut-off works best for you. And once the cutoff is determined just perform the cross validation with that value.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.