DEVELOPMENT... OpenML
Flow
skopt.searchcv.BayesSearchCV(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))

skopt.searchcv.BayesSearchCV(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 03-09-2018 by Jason sklearn==0.18.1 numpy>=1.6.1 scipy>=0.9 0 runs
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
  • openml-python python scikit-learn sklearn sklearn_0.18.1
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Automatically created scikit-learn flow.

Components

estimatorsklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier)(6)

Parameters

cvdefault: null
error_scoredefault: "raise"
estimatordefault: {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": null}}
fit_params
iiddefault: true
n_iterdefault: 32
n_jobsdefault: -1
n_pointsdefault: 1
optimizer_kwargsdefault: {"base_estimator": "RF"}
pre_dispatchdefault: "2*n_jobs"
random_statedefault: 42
refitdefault: true
return_train_scoredefault: false
scoringdefault: "accuracy"
search_spacesdefault: {"classifier__bootstrap": {"oml-python:serialized_object": "skopt_space", "value": "skopt.space.space.Categorical(categories=(True, False), prior=None)"}, "classifier__criterion": {"oml-python:serialized_object": "skopt_space", "value": "skopt.space.space.Categorical(categories=('gini', 'entropy'), prior=None)"}, "classifier__max_depth": {"oml-python:serialized_object": "skopt_space", "value": "skopt.space.space.Categorical(categories=(None,), prior=None)"}, "classifier__max_features": {"oml-python:serialized_object": "skopt_space", "value": "skopt.space.space.Real(low=0.001, high=1.0, prior='log-uniform', transform='identity')"}, "classifier__max_leaf_nodes": {"oml-python:serialized_object": "skopt_space", "value": "skopt.space.space.Categorical(categories=(None,), prior=None)"}, "classifier__min_samples_leaf": {"oml-python:serialized_object": "skopt_space", "value": "skopt.space.space.Integer(low=1, high=20)"}, "classifier__min_samples_split": {"oml-python:serialized_object": "skopt_space", "value": "skopt.space.space.Integer(low=2, high=20)"}, "classifier__min_weight_fraction_leaf": {"oml-python:serialized_object": "skopt_space", "value": "skopt.space.space.Categorical(categories=(0.0,), prior=None)"}, "classifier__n_estimators": {"oml-python:serialized_object": "skopt_space", "value": "skopt.space.space.Categorical(categories=(100,), prior=None)"}, "imputation__strategy": {"oml-python:serialized_object": "skopt_space", "value": "skopt.space.space.Categorical(categories=('mean', 'median', 'most_frequent'), prior=None)"}}
verbosedefault: 0

0
Runs

List all runs
Parameter:
Rendering chart
Rendering table