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sklearn.ensemble.voting_classifier.VotingClassifier(rf=sklearn.ensemble.forest.RandomForestClassifier)
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sklearn.ensemble.voting_classifier.VotingClassifier(rf=sklearn.ensemble.forest.RandomForestClassifier)
Visibility: public
Uploaded 09-03-2017 by
János Szedelényi
sklearn==0.18.1 numpy>=1.6.1 scipy>=0.9
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Automatically created scikit-learn flow.
Parameters
estimators
default: [{"oml-python:serialized_object": "component_reference", "value": {"step_name": "rf", "key": "rf"}}]
n_jobs
default: -1
voting
default: "soft"
weights
default: [1]
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Parameter:
none
estimators
n jobs
voting
weights
Supervised Classification
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area under roc curve
average cost
binominal test
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c index
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class complexity gain
confusion matrix
correlation coefficient
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coverage
f measure
information gain
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kohavi wolpert bias squared
kohavi wolpert error
kohavi wolpert sigma squared
kohavi wolpert variance
kononenko bratko information score
matthews correlation coefficient
mean absolute error
mean class complexity
mean class complexity gain
mean f measure
mean kononenko bratko information score
mean precision
mean prior absolute error
mean prior class complexity
mean recall
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mean weighted f measure
mean weighted precision
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root mean squared error
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