[MRG] FIX lasso/elasticnet example did not add noise to simulated data. #8427
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The first argument of np.random.normal is the mean of the distribution, and not the output shape. The example thus did not add noise but only an intercept to the model.
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Note also that this example does not run on python 3 (which I am happy to fix as well). |
Codecov Report
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Hits 57708 57708
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please fix the rest as well. thx @NelleV |
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Note also that this example does not run on python 3 (which I am happy to fix as well).
That would be cool.
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I believe this is fixed in master. To be slightly pedantic I think that is a combination of Python 3 + numpy 1.12 actually. |
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I looked at the generated example HTML and the plot looks extremely similar so LGTM. I pushed a minor change and I'll merge this once the CIs are green. Please ping if I forget. |
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OK CircleCI is passing, merging this one, thanks a lot! |
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It was indeed fixed on master. My master branch was not up-to-date :) |
sergeyf
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Feb 28, 2017
…a. (scikit-learn#8427) The first argument of np.random.normal is the mean of the distribution, and not the output shape. The example thus did not add noise but only an intercept to the model.
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Sundrique
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…a. (scikit-learn#8427) The first argument of np.random.normal is the mean of the distribution, and not the output shape. The example thus did not add noise but only an intercept to the model.
NelleV
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…a. (scikit-learn#8427) The first argument of np.random.normal is the mean of the distribution, and not the output shape. The example thus did not add noise but only an intercept to the model.
paulha
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Aug 19, 2017
…a. (scikit-learn#8427) The first argument of np.random.normal is the mean of the distribution, and not the output shape. The example thus did not add noise but only an intercept to the model.
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Nov 15, 2017
…a. (scikit-learn#8427) The first argument of np.random.normal is the mean of the distribution, and not the output shape. The example thus did not add noise but only an intercept to the model.
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Jan 6, 2021
…a. (scikit-learn#8427) The first argument of np.random.normal is the mean of the distribution, and not the output shape. The example thus did not add noise but only an intercept to the model.
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The first argument of np.random.normal is the mean of the distribution, and
not the output shape. The example thus did not add noise but only an intercept
to the model.
Considering the comment, I assume this is a mistake.