8000 [MRG] Example for multioutput.RegressorChain by timnon · Pull Request #15215 · scikit-learn/scikit-learn · GitHub
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[MRG] Example for multioutput.RegressorChain #15215

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timnon
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@timnon timnon commented Oct 12, 2019

Reference Issues/PRs

Issue #3846

What does this implement/fix? Explain your changes.

Example for multioutput.RegressorChain

Any other comments?

The naming of this class is wrong, it should be MultiLabelClassifierChain, it is not a regression but a multi-label classification

@timnon timnon changed the title example for multioutput.RegressorChain [MRG] Example for multioutput.RegressorChain Oct 12, 2019
@Malesche
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@adrinjalali open

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Thanks for the PR @timnon

wasn't there another PR adding an example to this class @Malesche ?

>>> from sklearn.multioutput import RegressorChain
>>> from sklearn.linear_model import LogisticRegression
>>> logreg = LogisticRegression(solver='lbfgs',multi_class='multinomial')
>>> X, Y = [[1,0],[0,1],[1,1]], [[0,2],[1,1],[2,0]]
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Suggested change
>>> X, Y = [[1,0],[0,1],[1,1]], [[0,2],[1,1],[2,0]]
>>> X, Y = [[1, 0], [0, 1], [1, 1]], [[0, 2], [1, 1], [2, 0]]

>>> from sklearn.linear_model import LogisticRegression
>>> logreg = LogisticRegression(solver='lbfgs',multi_class='multinomial')
>>> X, Y = [[1,0],[0,1],[1,1]], [[0,2],[1,1],[2,0]]
>>> chain = RegressorChain(base_estimator=logreg,order=[0,1]).fit(X,Y)
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Suggested change
>>> chain = RegressorChain(base_estimator=logreg,order=[0,1]).fit(X,Y)
>>> chain = RegressorChain(base_estimator=logreg, order=[0, 1]).fit(X, Y)

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@adrinjalali no, I don't think so

@Malesche Malesche mentioned this pull request Mar 6, 2020
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@marenwestermann
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@timnon do you plan to continue working on this PR?

@marenwestermann
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take

@LeandroLuque
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take

@cmarmo
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cmarmo commented Jun 17, 2020

Thanks @timnon for your work. I'm closing this one as #17498 has been merged in the meanwhile.

@cmarmo cmarmo closed this Jun 17, 2020
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