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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[MRG] Example for multioutput.RegressorChain #15215

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12 changes: 12 additions & 0 deletions sklearn/multioutput.py
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Expand Up @@ -734,6 +734,18 @@ class RegressorChain(MetaEstimatorMixin, RegressorMixin, _BaseChain):
order_ : list
The order of labels in the classifier chain.

Examples
--------
>>> 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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>>> 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]]

>>> chain = RegressorChain(base_estimator=logreg,order=[0,1]).fit(X,Y)
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>>> 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

>>> chain.predict(X)
array([[0., 2.],
[1., 1.],
[2., 0.]])

See also
--------
ClassifierChain: Equivalent for classification
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