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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 07-01-2018 by Jasmine Robinson sklearn==0.19.1 numpy>=1.6.1 scipy>=0.9 595 runs
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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)(1)

Parameters

cv		default: 2
error_score		default: "raise"
estimator		default: {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": null}}
fit_params		default: null
iid		default: true
n_iter		default: 1
n_jobs		default: 1
param_distributions		default: {"classifier__criterion": ["gini", "entropy"], "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_dispatch		default: "2*n_jobs"
random_state		default: 1
refit		default: true
return_train_score		default: "warn"
scoring		default: "accuracy"
verbose		default: 0

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