Flow
sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)

sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)

Visibility: public Uploaded 31-05-2018 by Jeroen van Hoof sklearn==0.19.1 numpy>=1.6.1 scipy>=0.9 555 runs
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  • openml-python python scikit-learn sklearn sklearn_0.19.1
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Automatically created scikit-learn flow.

Parameters

memorydefault: null
stepsdefault: [{"oml-python:serialized_object": "component_reference", "value": {"key": "conditionalimputer", "step_name": "conditionalimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "variancethreshold", "step_name": "variancethreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "randomforestclassifier", "step_name": "randomforestclassifier"}}]

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