Run 8395

Task 96 (Supervised Classification) credit-a Uploaded 25-11-2022 by Continuous Integration
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TESTf1313ba595sklearn.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``.
TESTf1313ba595sklearn.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
TESTf1313ba595sklearn.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"}}]
TESTf1313ba595sklearn.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
TESTf1313ba595sklearn.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
TESTf1313ba595sklearn.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"
TESTf1313ba595sklearn.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
TESTf1313ba595sklearn.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
TESTf1313ba595sklearn.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]}}]
TESTf1313ba595sklearn.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
TESTf1313ba595sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)(1)_memory	null
TESTf1313ba595sklearn.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"}}]
TESTf1313ba595sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)(1)_verbose	false
TESTf1313ba595sklearn.impute._base.SimpleImputer(1)_add_indicator	false
TESTf1313ba595sklearn.impute._base.SimpleImputer(1)_copy	true
TESTf1313ba595sklearn.impute._base.SimpleImputer(1)_fill_value	null
TESTf1313ba595sklearn.impute._base.SimpleImputer(1)_missing_values	NaN
TESTf1313ba595sklearn.impute._base.SimpleImputer(1)_strategy	"mean"
TESTf1313ba595sklearn.impute._base.SimpleImputer(1)_verbose	0
TESTf1313ba595sklearn.preprocessing._data.StandardScaler(1)_copy	true
TESTf1313ba595sklearn.preprocessing._data.StandardScaler(1)_with_mean	true
TESTf1313ba595sklearn.preprocessing._data.StandardScaler(1)_with_std	true
TESTf1313ba595sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_memory	null
TESTf1313ba595sklearn.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"}}]
TESTf1313ba595sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_verbose	false
TESTf1313ba595openml.testing.CustomImputer(1)_add_indicator	false
TESTf1313ba595openml.testing.CustomImputer(1)_copy	true
TESTf1313ba595openml.testing.CustomImputer(1)_fill_value	null
TESTf1313ba595openml.testing.CustomImputer(1)_missing_values	NaN
TESTf1313ba595openml.testing.CustomImputer(1)_strategy	"most_frequent"
TESTf1313ba595openml.testing.CustomImputer(1)_verbose	0
TESTf1313ba595sklearn.preprocessing._encoders.OneHotEncoder(1)_categories	"auto"
TESTf1313ba595sklearn.preprocessing._encoders.OneHotEncoder(1)_drop	null
TESTf1313ba595sklearn.preprocessing._encoders.OneHotEncoder(1)_dtype	{"oml-python:serialized_object": "type", "value": "np.float64"}
TESTf1313ba595sklearn.preprocessing._encoders.OneHotEncoder(1)_handle_unknown	"ignore"
TESTf1313ba595sklearn.preprocessing._encoders.OneHotEncoder(1)_sparse	true
TESTf1313ba595sklearn.tree._classes.DecisionTreeClassifier(1)_ccp_alpha	0.0
TESTf1313ba595sklearn.tree._classes.DecisionTreeClassifier(1)_class_weight	null
TESTf1313ba595sklearn.tree._classes.DecisionTreeClassifier(1)_criterion	"gini"
TESTf1313ba595sklearn.tree._classes.DecisionTreeClassifier(1)_max_depth	null
TESTf1313ba595sklearn.tree._classes.DecisionTreeClassifier(1)_max_features	null
TESTf1313ba595sklearn.tree._classes.DecisionTreeClassifier(1)_max_leaf_nodes	null
TESTf1313ba595sklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_decrease	0.0
TESTf1313ba595sklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_split	null
TESTf1313ba595sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_leaf	1
TESTf1313ba595sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_split	2
TESTf1313ba595sklearn.tree._classes.DecisionTreeClassifier(1)_min_weight_fraction_leaf	0.0
TESTf1313ba595sklearn.tree._classes.DecisionTreeClassifier(1)_random_state	62501
TESTf1313ba595sklearn.tree._classes.DecisionTreeClassifier(1)_splitter	"best"