DEVELOPMENT... OpenML
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sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.tree.tree.DecisionTreeClassifier))

sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.tree.tree.DecisionTreeClassifier))

Visibility: public Uploaded 29-05-2018 by Jasmine Robinson sklearn==0.19.1 numpy>=1.6.1 scipy>=0.9 0 runs
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  • openml-python python scikit-learn sklearn sklearn_0.19.1
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Automatically created scikit-learn flow.

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Parameters

cvdefault: 3
error_scoredefault: "raise"
estimatordefault: {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": null}}
fit_paramsdefault: null
iiddefault: true
n_iterdefault: 250
n_jobsdefault: -1
param_distributionsdefault: {"classifier__criterion": ["gini", "entropy"], "classifier__max_depth": [2, 3, 5, 7, 10], "classifier__max_features": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0], "imputation__strategy": ["mean", "median", "most_frequent"]}
pre_dispatchdefault: "2*n_jobs"
random_statedefault: 3
refitdefault: true
return_train_scoredefault: "warn"
scoringdefault: null
verbosedefault: 0

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