Run 10202763

Task 3889 (Supervised Classification) sylva_agnostic Uploaded 17-04-2019 by Jan van Rijn
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Flow

sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transfo rmer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.pr eprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.St andardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.imput e.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder )),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceT hreshold,gradientboostingclassifier=sklearn.ensemble.gradient_boosting.Grad ientBoostingClassifier)(4)	Automatically created scikit-learn flow.
sklearn.impute.SimpleImputer(10)_copy	true
sklearn.impute.SimpleImputer(10)_fill_value	-1
sklearn.impute.SimpleImputer(10)_missing_values	NaN
sklearn.impute.SimpleImputer(10)_strategy	"constant"
sklearn.impute.SimpleImputer(10)_verbose	0
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))(4)_n_jobs	null
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))(4)_remainder	"passthrough"
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))(4)_sparse_threshold	0.3
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))(4)_transformer_weights	null
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))(4)_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, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "nominal", "step_name": "nominal", "argument_1": []}}]
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(4)_memory	null
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(4)_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"}}]
sklearn.preprocessing.imputation.Imputer(38)_axis	0
sklearn.preprocessing.imputation.Imputer(38)_copy	true
sklearn.preprocessing.imputation.Imputer(38)_missing_values	"NaN"
sklearn.preprocessing.imputation.Imputer(38)_strategy	"most_frequent"
sklearn.preprocessing.imputation.Imputer(38)_verbose	0
sklearn.preprocessing.data.StandardScaler(25)_copy	true
sklearn.preprocessing.data.StandardScaler(25)_with_mean	true
sklearn.preprocessing.data.StandardScaler(25)_with_std	true
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(4)_memory	null
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(4)_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"}}]
sklearn.preprocessing._encoders.OneHotEncoder(9)_categorical_features	null
sklearn.preprocessing._encoders.OneHotEncoder(9)_categories	null
sklearn.preprocessing._encoders.OneHotEncoder(9)_dtype	{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(9)_handle_unknown	"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(9)_n_values	null
sklearn.preprocessing._encoders.OneHotEncoder(9)_sparse	true
sklearn.feature_selection.variance_threshold.VarianceThreshold(24)_threshold	0.0
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,gradientboostingclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)(4)_memory	null
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,gradientboostingclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)(4)_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": "gradientboostingclassifier", "step_name": "gradientboostingclassifier"}}]
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_criterion	"friedman_mse"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_init	null
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_learning_rate	1.62553077364939e-05
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_loss	"deviance"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_max_depth	22
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_max_features	0.7726159126198059
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_max_leaf_nodes	null
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_min_impurity_decrease	0.5417163893328134
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_min_impurity_split	null
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_min_samples_leaf	10
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_min_samples_split	8
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_min_weight_fraction_leaf	0.4501598696311843
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_n_estimators	303
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_n_iter_no_change	1108
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_presort	"auto"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_random_state	13783
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_subsample	0.6829334490139528
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_tol	0.012341494228373395
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_validation_fraction	0.05313288980598252
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_verbose	0
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_warm_start	false