[scikit-learn] biased predictions in logistic regression

[Stuart Reynolds]

Here's a discussion

http://stats.stackexchange.com/questions/6067/does-an-unbalanced-sample-matter-when-doing-logistic-regression

See the Zheng and King reference.
It would be nice to have these methods in scikit.



On Thu, Dec 15, 2016 at 7:05 PM Rachel Melamed <melamed at uchicago.edu> wrote:

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> Stuart,
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> Yes the data is quite imbalanced (this is what I meant by p(success) < .05
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> To be clear, I calculate
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> \sum_i \hat{y_i}
> = logregN.predict_proba(design)[:,1]*(success_fail.sum(axis=1))
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> and compare that number to the observed number of success. I find the
> predicted number to always be higher (I think, because of the intercept).
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> I was not aware of a bias for imbalanced data.  Can you tell me more? Why
> does it not appear with the relaxed regularization? Also, using the same
> data with statsmodels LR, which has no regularization, this doesn't seem to
> be a problem. Any suggestions for
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> how I could fix this are welcome.
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> Thank you
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> On Dec 15, 2016, at 4:41 PM, Stuart Reynolds <stuart at stuartreynolds.net>
> wrote:
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> LR is biased with imbalanced datasets. Is your dataset unbalanced? (e.g.
> is there one class that has a much smaller prevalence in the data that the
> other)?
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> On Thu, Dec 15, 2016 at 1:02 PM, Rachel Melamed
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> <melamed at uchicago.edu> wrote:
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> I just tried it and it did not appear to change the results at all?
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> I ran it as follows:
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> 1) Normalize dummy variables (by subtracting median) to make a matrix of
> about 10000 x 5
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> 2) For each of the 1000 output variables:
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> a. Each output variable uses the same dummy variables, but not all
> settings of covariates are observed for all output variables. So I create
> the design matrix using patsy per output variable to include pairwise
> interactions.  Then, I have an around
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> 10000 x 350 design matrix , and a matrix I call “success_fail” that has
> for each setting the number of success and number of fail, so it is of size
> 10000 x 2
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> b. Run regression using:
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> skdesign = np.vstack((design,design))
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> sklabel = np.hstack((np.ones(success_fail.shape[0]),
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> np.zeros(success_fail.shape[0])))
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> skweight = np.hstack((success_fail['success'], success_fail['fail']))
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>         logregN = linear_model.LogisticRegression(C=1,
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>                                     solver= 'lbfgs',fit_intercept=False)
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>         logregN.fit(skdesign, sklabel, sample_weight=skweight)
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> On Dec 15, 2016, at 2:16 PM, Alexey Dral <aadral at gmail.com> wrote:
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> Could you try to normalize dataset after feature dummy encoding and see if
> it is reproducible behavior?
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> 2016-12-15 22:03 GMT+03:00 Rachel Melamed
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> <melamed at uchicago.edu>:
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> Thanks for the reply.  The covariates (“X") are all dummy/categorical
> variables.  So I guess no, nothing is normalized.
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> On Dec 15, 2016, at 1:54 PM, Alexey Dral <aadral at gmail.com> wrote:
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> Hi Rachel,
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> Do you have your data normalized?
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> 2016-12-15 20:21 GMT+03:00 Rachel Melamed
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> <melamed at uchicago.edu>:
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> Hi all,
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> Does anyone have any suggestions for this problem:
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> http://stackoverflow.com/questions/41125342/sklearn-logistic-regression-gives-biased-results
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> 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).
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> 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
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> the more-regularized model does seem a bit better.
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> 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>
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> 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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> _______________________________________________
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> --
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> Yours sincerely,
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> Alexey A. Dral
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> --
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> Yours sincerely,
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> Alexey A. Dral
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