Run 790

Task 96 (Supervised Classification) credit-a Uploaded 03-07-2024 by Continuous Integration
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TEST1fd180a8c8sklearn.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``.
TEST1fd180a8c8sklearn.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
TEST1fd180a8c8sklearn.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"}}]
TEST1fd180a8c8sklearn.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
TEST1fd180a8c8sklearn.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
TEST1fd180a8c8sklearn.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"
TEST1fd180a8c8sklearn.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
TEST1fd180a8c8sklearn.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
TEST1fd180a8c8sklearn.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]}}]
TEST1fd180a8c8sklearn.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
TEST1fd180a8c8sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)(1)_memory	null
TEST1fd180a8c8sklearn.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"}}]
TEST1fd180a8c8sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)(1)_verbose	false
TEST1fd180a8c8sklearn.impute._base.SimpleImputer(1)_add_indicator	false
TEST1fd180a8c8sklearn.impute._base.SimpleImputer(1)_copy	true
TEST1fd180a8c8sklearn.impute._base.SimpleImputer(1)_fill_value	null
TEST1fd180a8c8sklearn.impute._base.SimpleImputer(1)_missing_values	NaN
TEST1fd180a8c8sklearn.impute._base.SimpleImputer(1)_strategy	"mean"
TEST1fd180a8c8sklearn.impute._base.SimpleImputer(1)_verbose	0
TEST1fd180a8c8sklearn.preprocessing._data.StandardScaler(1)_copy	true
TEST1fd180a8c8sklearn.preprocessing._data.StandardScaler(1)_with_mean	true
TEST1fd180a8c8sklearn.preprocessing._data.StandardScaler(1)_with_std	true
TEST1fd180a8c8sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_memory	null
TEST1fd180a8c8sklearn.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"}}]
TEST1fd180a8c8sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_verbose	false
TEST1fd180a8c8openml.testing.CustomImputer(1)_add_indicator	false
TEST1fd180a8c8openml.testing.CustomImputer(1)_copy	true
TEST1fd180a8c8openml.testing.CustomImputer(1)_fill_value	null
TEST1fd180a8c8openml.testing.CustomImputer(1)_missing_values	NaN
TEST1fd180a8c8openml.testing.CustomImputer(1)_strategy	"most_frequent"
TEST1fd180a8c8openml.testing.CustomImputer(1)_verbose	0
TEST1fd180a8c8sklearn.preprocessing._encoders.OneHotEncoder(1)_categories	"auto"
TEST1fd180a8c8sklearn.preprocessing._encoders.OneHotEncoder(1)_drop	null
TEST1fd180a8c8sklearn.preprocessing._encoders.OneHotEncoder(1)_dtype	{"oml-python:serialized_object": "type", "value": "np.float64"}
TEST1fd180a8c8sklearn.preprocessing._encoders.OneHotEncoder(1)_handle_unknown	"ignore"
TEST1fd180a8c8sklearn.preprocessing._encoders.OneHotEncoder(1)_sparse	true
TEST1fd180a8c8sklearn.tree._classes.DecisionTreeClassifier(1)_ccp_alpha	0.0
TEST1fd180a8c8sklearn.tree._classes.DecisionTreeClassifier(1)_class_weight	null
TEST1fd180a8c8sklearn.tree._classes.DecisionTreeClassifier(1)_criterion	"gini"
TEST1fd180a8c8sklearn.tree._classes.DecisionTreeClassifier(1)_max_depth	null
TEST1fd180a8c8sklearn.tree._classes.DecisionTreeClassifier(1)_max_features	null
TEST1fd180a8c8sklearn.tree._classes.DecisionTreeClassifier(1)_max_leaf_nodes	null
TEST1fd180a8c8sklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_decrease	0.0
TEST1fd180a8c8sklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_split	null
TEST1fd180a8c8sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_leaf	1
TEST1fd180a8c8sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_split	2
TEST1fd180a8c8sklearn.tree._classes.DecisionTreeClassifier(1)_min_weight_fraction_leaf	0.0
TEST1fd180a8c8sklearn.tree._classes.DecisionTreeClassifier(1)_presort	"deprecated"
TEST1fd180a8c8sklearn.tree._classes.DecisionTreeClassifier(1)_random_state	62501
TEST1fd180a8c8sklearn.tree._classes.DecisionTreeClassifier(1)_splitter	"best"