Run 9548858

Task 9967 (Supervised Classification) steel-plates-fault Uploaded 11-10-2018 by Jan van Rijn
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Flow

sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transfo rmer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=s klearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imp uter,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=skle arn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotenco der=sklearn.preprocessing._encoders.OneHotEncoder)),extratreesclassifier=sk learn.ensemble.forest.ExtraTreesClassifier)(1)	Automatically created scikit-learn flow.
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(1)_n_jobs	null
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(1)_remainder	"passthrough"
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(1)_sparse_threshold	0.3
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(1)_transformer_weights	null
sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(1)_memory	null
sklearn.impute.MissingIndicator(1)_error_on_new	false
sklearn.impute.MissingIndicator(1)_features	"missing-only"
sklearn.impute.MissingIndicator(1)_missing_values	NaN
sklearn.impute.MissingIndicator(1)_sparse	"auto"
sklearn.preprocessing.imputation.Imputer(29)_axis	0
sklearn.preprocessing.imputation.Imputer(29)_copy	true
sklearn.preprocessing.imputation.Imputer(29)_missing_values	"NaN"
sklearn.preprocessing.imputation.Imputer(29)_strategy	"median"
sklearn.preprocessing.imputation.Imputer(29)_verbose	0
sklearn.preprocessing.data.StandardScaler(14)_copy	true
sklearn.preprocessing.data.StandardScaler(14)_with_mean	true
sklearn.preprocessing.data.StandardScaler(14)_with_std	true
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_memory	null
sklearn.impute.SimpleImputer(1)_copy	true
sklearn.impute.SimpleImputer(1)_fill_value	-1
sklearn.impute.SimpleImputer(1)_missing_values	NaN
sklearn.impute.SimpleImputer(1)_strategy	"constant"
sklearn.impute.SimpleImputer(1)_verbose	0
sklearn.preprocessing._encoders.OneHotEncoder(3)_categorical_features	null
sklearn.preprocessing._encoders.OneHotEncoder(3)_categories	null
sklearn.preprocessing._encoders.OneHotEncoder(3)_dtype	{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(3)_handle_unknown	"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(3)_n_values	null
sklearn.preprocessing._encoders.OneHotEncoder(3)_sparse	true
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),extratreesclassifier=sklearn.ensemble.forest.ExtraTreesClassifier)(1)_memory	null
sklearn.ensemble.forest.ExtraTreesClassifier(10)_bootstrap	true
sklearn.ensemble.forest.ExtraTreesClassifier(10)_class_weight	null
sklearn.ensemble.forest.ExtraTreesClassifier(10)_criterion	"gini"
sklearn.ensemble.forest.ExtraTreesClassifier(10)_max_depth	null
sklearn.ensemble.forest.ExtraTreesClassifier(10)_max_features	0.24968530140924106
sklearn.ensemble.forest.ExtraTreesClassifier(10)_max_leaf_nodes	null
sklearn.ensemble.forest.ExtraTreesClassifier(10)_min_impurity_decrease	0.0
sklearn.ensemble.forest.ExtraTreesClassifier(10)_min_impurity_split	null
sklearn.ensemble.forest.ExtraTreesClassifier(10)_min_samples_leaf	11
sklearn.ensemble.forest.ExtraTreesClassifier(10)_min_samples_split	4
sklearn.ensemble.forest.ExtraTreesClassifier(10)_min_weight_fraction_leaf	0.0
sklearn.ensemble.forest.ExtraTreesClassifier(10)_n_estimators	100
sklearn.ensemble.forest.ExtraTreesClassifier(10)_n_jobs	null
sklearn.ensemble.forest.ExtraTreesClassifier(10)_oob_score	false
sklearn.ensemble.forest.ExtraTreesClassifier(10)_random_state	45354
sklearn.ensemble.forest.ExtraTreesClassifier(10)_verbose	0
sklearn.ensemble.forest.ExtraTreesClassifier(10)_warm_start	false