8817 6892 sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,svc=sklearn.svm.classes.SVC) sklearn.pipeline.Pipeline 1 openml==0.8.0dev,sklearn==0.20.0 Automatically created scikit-learn flow. 2018-10-20T00:56:11 English sklearn==0.20.0 numpy>=1.6.1 scipy>=0.9 memory null steps [{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "variancethreshold", "step_name": "variancethreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "svc", "step_name": "svc"}}] svc 8785 1 sklearn.svm.classes.SVC sklearn.svm.classes.SVC 22 openml==0.8.0dev,sklearn==0.20.0 Automatically created scikit-learn flow. 2018-10-09T02:18:13 English sklearn==0.20.0 numpy>=1.6.1 scipy>=0.9 C 143.11841724167172 cache_size 200 class_weight null coef0 0.22921525238301776 decision_function_shape "ovr" degree 2 gamma 0.0070192306738709525 kernel "poly" max_iter -1 probability false random_state null shrinking false tol 1.1571247532842242e-05 verbose false openml-python python scikit-learn sklearn sklearn_0.20.0 columntransformer 8812 1 sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)) sklearn.compose._column_transformer.ColumnTransformer 1 openml==0.8.0dev,sklearn==0.20.0 Automatically created scikit-learn flow. 2018-10-18T22:00:43 English sklearn==0.20.0 numpy>=1.6.1 scipy>=0.9 n_jobs null remainder "passthrough" sparse_threshold 0.3 transformer_weights null transformers [{"oml-python:serialized_object": "component_reference", "value": {"key": "numeric", "step_name": "numeric", "argument_1": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "nominal", "step_name": "nominal", "argument_1": []}}] nominal 8780 1 sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder) sklearn.pipeline.Pipeline 1 openml==0.8.0dev,sklearn==0.20.0 Automatically created scikit-learn flow. 2018-10-07T23:00:45 English sklearn==0.20.0 numpy>=1.6.1 scipy>=0.9 memory null steps [{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}] simpleimputer 8781 1 sklearn.impute.SimpleImputer sklearn.impute.SimpleImputer 1 openml==0.8.0dev,sklearn==0.20.0 Automatically created scikit-learn flow. 2018-10-07T23:00:45 English sklearn==0.20.0 numpy>=1.6.1 scipy>=0.9 copy true fill_value -1 missing_values NaN strategy "constant" verbose 0 openml-python python scikit-learn sklearn sklearn_0.20.0 onehotencoder 8782 1 sklearn.preprocessing._encoders.OneHotEncoder sklearn.preprocessing._encoders.OneHotEncoder 3 openml==0.8.0dev,sklearn==0.20.0 Automatically created scikit-learn flow. 2018-10-07T23:00:45 English sklearn==0.20.0 numpy>=1.6.1 scipy>=0.9 categorical_features null categories null dtype {"oml-python:serialized_object": "type", "value": "np.float64"} handle_unknown "ignore" n_values null sparse true openml-python python scikit-learn sklearn sklearn_0.20.0 openml-python python scikit-learn sklearn sklearn_0.20.0 numeric 8813 1 sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler) sklearn.pipeline.Pipeline 1 openml==0.8.0dev,sklearn==0.20.0 Automatically created scikit-learn flow. 2018-10-18T22:00:43 English sklearn==0.20.0 numpy>=1.6.1 scipy>=0.9 memory null steps [{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}] imputer 8778 1 sklearn.preprocessing.imputation.Imputer sklearn.preprocessing.imputation.Imputer 29 openml==0.8.0dev,sklearn==0.20.0 Automatically created scikit-learn flow. 2018-10-07T23:00:45 English sklearn==0.20.0 numpy>=1.6.1 scipy>=0.9 axis 0 copy true missing_values "NaN" strategy "mean" verbose 0 openml-python python scikit-learn sklearn sklearn_0.20.0 standardscaler 8779 1 sklearn.preprocessing.data.StandardScaler sklearn.preprocessing.data.StandardScaler 14 openml==0.8.0dev,sklearn==0.20.0 Automatically created scikit-learn flow. 2018-10-07T23:00:45 English sklearn==0.20.0 numpy>=1.6.1 scipy>=0.9 copy true with_mean true with_std true openml-python python scikit-learn sklearn sklearn_0.20.0 openml-python python scikit-learn sklearn sklearn_0.20.0 openml-python python scikit-learn sklearn sklearn_0.20.0 variancethreshold 8816 1 sklearn.feature_selection.variance_threshold.VarianceThreshold sklearn.feature_selection.variance_threshold.VarianceThreshold 18 openml==0.8.0dev,sklearn==0.20.0 Automatically created scikit-learn flow. 2018-10-19T02:03:47 English sklearn==0.20.0 numpy>=1.6.1 scipy>=0.9 threshold 0.0 openml-python python scikit-learn sklearn sklearn_0.20.0 openml-python python scikit-learn sklearn sklearn_0.20.0