[scikit-learn] biased predictions in logistic regression
aadral at gmail.com
Thu Dec 15 13:54:44 EST 2016
Do you have your data normalized?
2016-12-15 20:21 GMT+03:00 Rachel Melamed <melamed at uchicago.edu>:
> Hi all,
> Does anyone have any suggestions for this problem:
> I am running around 1000 similar logistic regressions, with the same
> covariates but slightly different data and response variables. All of my
> response variables have a sparse successes (p(success) < .05 usually).
> I noticed that with the regularized regression, the results are
> consistently biased to predict more "successes" than is observed in the
> training data. When I relax the regularization, this bias goes away. The
> bias observed is unacceptable for my use case, but the more-regularized
> model does seem a bit better.
> Below, I plot the results for the 1000 different regressions for 2
> different values of C: [image: results for the different regressions for
> 2 different values of C] <https://i.stack.imgur.com/1cbrC.png>
> I looked at the parameter estimates for one of these regressions: below
> each point is one parameter. It seems like the intercept (the point on the
> bottom left) is too high for the C=1 model. [image: enter image
> description here] <https://i.stack.imgur.com/NTFOY.png>
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> scikit-learn at python.org
Alexey A. Dral
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