8351 1935 sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf=sklearn.ensemble.forest.RandomForestClassifier) sklearn.pipeline.Pipeline 1 hyperimp==0.0.1,openml==0.6.0,sklearn==0.19.1 Automatically created scikit-learn flow. 2018-04-14T05:26:12 English sklearn==0.19.1 numpy>=1.6.1 scipy>=0.9 memory null steps [{"oml-python:serialized_object": "component_reference", "value": {"key": "imputation", "step_name": "imputation"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "hotencoding", "step_name": "hotencoding"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "variencethreshold", "step_name": "variencethreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "clf", "step_name": "clf"}}] hotencoding 7644 3886 sklearn.preprocessing.data.OneHotEncoder sklearn.preprocessing.data.OneHotEncoder 17 openml==0.6.0,sklearn==0.19.1 Automatically created scikit-learn flow. 2017-11-14T19:26:22 English sklearn==0.19.1 numpy>=1.6.1 scipy>=0.9 categorical_features [0, 1, 2, 3, 4, 5, 6, 7, 8] dtype {"oml-python:serialized_object": "type", "value": "np.float64"} handle_unknown "ignore" n_values "auto" sparse true openml-python python scikit-learn sklearn sklearn_0.19.1 variencethreshold 7645 3886 sklearn.feature_selection.variance_threshold.VarianceThreshold sklearn.feature_selection.variance_threshold.VarianceThreshold 11 openml==0.6.0,sklearn==0.19.1 Automatically created scikit-learn flow. 2017-11-14T19:26:22 English sklearn==0.19.1 numpy>=1.6.1 scipy>=0.9 threshold 0.0 openml-python python scikit-learn sklearn sklearn_0.19.1 clf 7684 4404 sklearn.ensemble.forest.RandomForestClassifier sklearn.ensemble.forest.RandomForestClassifier 32 openml==0.6.0,sklearn==0.19.1 Automatically created scikit-learn flow. 2017-12-12T01:04:32 English sklearn==0.19.1 numpy>=1.6.1 scipy>=0.9 bootstrap true class_weight null criterion "gini" max_depth null max_features "auto" max_leaf_nodes null min_impurity_decrease 0.0 min_impurity_split null min_samples_leaf 1 min_samples_split 2 min_weight_fraction_leaf 0.0 n_estimators 10 n_jobs 1 oob_score false random_state null verbose 0 warm_start false openml-python python scikit-learn sklearn sklearn_0.19.1 Verified_Supervised_Classification imputation 8352 1935 hyperimp.utils.preprocessing.ConditionalImputer2 hyperimp.utils.preprocessing.ConditionalImputer2 1 hyperimp==0.0.1,openml==0.6.0 Automatically created scikit-learn flow. 2018-04-14T05:26:12 English sklearn==0.19.1 numpy>=1.6.1 scipy>=0.9 axis 0 categorical_features [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35] copy true fill_empty 0 missing_values "NaN" strategy "mean" strategy_nominal "most_frequent" verbose 0 openml-python python scikit-learn sklearn sklearn_0.19.1 openml-python python scikit-learn sklearn sklearn_0.19.1 study_98