Run 9139

Task 96 (Supervised Classification) credit-a Uploaded 25-11-2022 by Continuous Integration
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TESTae4d889f42sklearn.pipeline.Pipeline(transformer=sklearn.compose._column _transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimpu ter=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing ._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=open ml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneH otEncoder)),classifier=sklearn.tree._classes.DecisionTreeClassifier)(1)	Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit and transform methods. The final estimator only needs to implement fit. The transformers in the pipeline can be cached using ``memory`` argument. The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a '__', as in the example below. A step's estimator may be replaced entirely by setting the parameter with its name to another estimator, or a transformer removed by setting it to 'passthrough' or ``None``.
TESTae4d889f42sklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=sklearn.tree._classes.DecisionTreeClassifier)(1)_memory	null
TESTae4d889f42sklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=sklearn.tree._classes.DecisionTreeClassifier)(1)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "transformer", "step_name": "transformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "classifier", "step_name": "classifier"}}]
TESTae4d889f42sklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	false
TESTae4d889f42sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(1)_n_jobs	null
TESTae4d889f42sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(1)_remainder	"passthrough"
TESTae4d889f42sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(1)_sparse_threshold	0.3
TESTae4d889f42sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(1)_transformer_weights	null
TESTae4d889f42sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(1)_transformers	[{"oml-python:serialized_object": "component_reference", "value": {"key": "numeric", "step_name": "numeric", "argument_1": [1, 2, 7, 10, 13, 14]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "nominal", "step_name": "nominal", "argument_1": [0, 3, 4, 5, 6, 8, 9, 11, 12]}}]
TESTae4d889f42sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(1)_verbose	false
TESTae4d889f42sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)(1)_memory	null
TESTae4d889f42sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)(1)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}]
TESTae4d889f42sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)(1)_verbose	false
TESTae4d889f42sklearn.impute._base.SimpleImputer(1)_add_indicator	false
TESTae4d889f42sklearn.impute._base.SimpleImputer(1)_copy	true
TESTae4d889f42sklearn.impute._base.SimpleImputer(1)_fill_value	null
TESTae4d889f42sklearn.impute._base.SimpleImputer(1)_missing_values	NaN
TESTae4d889f42sklearn.impute._base.SimpleImputer(1)_strategy	"mean"
TESTae4d889f42sklearn.impute._base.SimpleImputer(1)_verbose	0
TESTae4d889f42sklearn.preprocessing._data.StandardScaler(1)_copy	true
TESTae4d889f42sklearn.preprocessing._data.StandardScaler(1)_with_mean	true
TESTae4d889f42sklearn.preprocessing._data.StandardScaler(1)_with_std	true
TESTae4d889f42sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_memory	null
TESTae4d889f42sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "customimputer", "step_name": "customimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}]
TESTae4d889f42sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_verbose	false
TESTae4d889f42openml.testing.CustomImputer(1)_add_indicator	false
TESTae4d889f42openml.testing.CustomImputer(1)_copy	true
TESTae4d889f42openml.testing.CustomImputer(1)_fill_value	null
TESTae4d889f42openml.testing.CustomImputer(1)_missing_values	NaN
TESTae4d889f42openml.testing.CustomImputer(1)_strategy	"most_frequent"
TESTae4d889f42openml.testing.CustomImputer(1)_verbose	0
TESTae4d889f42sklearn.preprocessing._encoders.OneHotEncoder(1)_categories	"auto"
TESTae4d889f42sklearn.preprocessing._encoders.OneHotEncoder(1)_drop	null
TESTae4d889f42sklearn.preprocessing._encoders.OneHotEncoder(1)_dtype	{"oml-python:serialized_object": "type", "value": "np.float64"}
TESTae4d889f42sklearn.preprocessing._encoders.OneHotEncoder(1)_handle_unknown	"ignore"
TESTae4d889f42sklearn.preprocessing._encoders.OneHotEncoder(1)_sparse	true
TESTae4d889f42sklearn.tree._classes.DecisionTreeClassifier(1)_ccp_alpha	0.0
TESTae4d889f42sklearn.tree._classes.DecisionTreeClassifier(1)_class_weight	null
TESTae4d889f42sklearn.tree._classes.DecisionTreeClassifier(1)_criterion	"gini"
TESTae4d889f42sklearn.tree._classes.DecisionTreeClassifier(1)_max_depth	null
TESTae4d889f42sklearn.tree._classes.DecisionTreeClassifier(1)_max_features	null
TESTae4d889f42sklearn.tree._classes.DecisionTreeClassifier(1)_max_leaf_nodes	null
TESTae4d889f42sklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_decrease	0.0
TESTae4d889f42sklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_split	null
TESTae4d889f42sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_leaf	1
TESTae4d889f42sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_split	2
TESTae4d889f42sklearn.tree._classes.DecisionTreeClassifier(1)_min_weight_fraction_leaf	0.0
TESTae4d889f42sklearn.tree._classes.DecisionTreeClassifier(1)_random_state	62501
TESTae4d889f42sklearn.tree._classes.DecisionTreeClassifier(1)_splitter	"best"