Flow
openmlpimp.sklearn.beam_search.BeamSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier))

openmlpimp.sklearn.beam_search.BeamSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier))

Visibility: public Uploaded 04-08-2017 by Jan van Rijn sklearn==0.18.1 numpy>=1.6.1 scipy>=0.9 218 runs
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  • openml-python python scikit-learn sklearn sklearn_0.18.1 Verified_Supervised_Classification
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Automatically created scikit-learn flow.

Components

Parameters

beam_widthdefault: 1
cvdefault: null
error_scoredefault: "raise"
estimatordefault: {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": null}}
fit_params
iiddefault: true
n_jobsdefault: 1
param_distributionsdefault: {"classifier__min_samples_leaf": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], "classifier__max_features": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9], "classifier__bootstrap": [true, false], "classifier__min_samples_split": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], "classifier__criterion": ["gini", "entropy"], "imputation__strategy": ["mean", "median", "most_frequent"]}
pre_dispatchdefault: "2*n_jobs"
refitdefault: true
scoringdefault: {"oml-python:serialized_object": "function", "value": "sklearn.metrics.scorer._passthrough_scorer"}
verbosedefault: 0

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