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Run 3378

Task 119 (Supervised Classification) diabetes Uploaded 12-01-2024 by Continuous Integration
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TEST0b9e0b5718sklearn.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``.
TEST0b9e0b5718sklearn.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
TEST0b9e0b5718sklearn.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"}}]
TEST0b9e0b5718sklearn.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
TEST0b9e0b5718sklearn.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
TEST0b9e0b5718sklearn.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"
TEST0b9e0b5718sklearn.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
TEST0b9e0b5718sklearn.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
TEST0b9e0b5718sklearn.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": [0, 1, 2, 3, 4, 5, 6, 7]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "nominal", "step_name": "nominal", "argument_1": []}}]
TEST0b9e0b5718sklearn.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
TEST0b9e0b5718sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)(1)_memory	null
TEST0b9e0b5718sklearn.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"}}]
TEST0b9e0b5718sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)(1)_verbose	false
TEST0b9e0b5718sklearn.impute._base.SimpleImputer(1)_add_indicator	false
TEST0b9e0b5718sklearn.impute._base.SimpleImputer(1)_copy	true
TEST0b9e0b5718sklearn.impute._base.SimpleImputer(1)_fill_value	null
TEST0b9e0b5718sklearn.impute._base.SimpleImputer(1)_missing_values	NaN
TEST0b9e0b5718sklearn.impute._base.SimpleImputer(1)_strategy	"mean"
TEST0b9e0b5718sklearn.impute._base.SimpleImputer(1)_verbose	0
TEST0b9e0b5718sklearn.preprocessing._data.StandardScaler(1)_copy	true
TEST0b9e0b5718sklearn.preprocessing._data.StandardScaler(1)_with_mean	true
TEST0b9e0b5718sklearn.preprocessing._data.StandardScaler(1)_with_std	true
TEST0b9e0b5718sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_memory	null
TEST0b9e0b5718sklearn.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"}}]
TEST0b9e0b5718sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_verbose	false
TEST0b9e0b5718openml.testing.CustomImputer(1)_add_indicator	false
TEST0b9e0b5718openml.testing.CustomImputer(1)_copy	true
TEST0b9e0b5718openml.testing.CustomImputer(1)_fill_value	null
TEST0b9e0b5718openml.testing.CustomImputer(1)_missing_values	NaN
TEST0b9e0b5718openml.testing.CustomImputer(1)_strategy	"most_frequent"
TEST0b9e0b5718openml.testing.CustomImputer(1)_verbose	0
TEST0b9e0b5718sklearn.preprocessing._encoders.OneHotEncoder(1)_categories	"auto"
TEST0b9e0b5718sklearn.preprocessing._encoders.OneHotEncoder(1)_drop	null
TEST0b9e0b5718sklearn.preprocessing._encoders.OneHotEncoder(1)_dtype	{"oml-python:serialized_object": "type", "value": "np.float64"}
TEST0b9e0b5718sklearn.preprocessing._encoders.OneHotEncoder(1)_handle_unknown	"ignore"
TEST0b9e0b5718sklearn.preprocessing._encoders.OneHotEncoder(1)_sparse	true
TEST0b9e0b5718sklearn.tree._classes.DecisionTreeClassifier(1)_ccp_alpha	0.0
TEST0b9e0b5718sklearn.tree._classes.DecisionTreeClassifier(1)_class_weight	null
TEST0b9e0b5718sklearn.tree._classes.DecisionTreeClassifier(1)_criterion	"gini"
TEST0b9e0b5718sklearn.tree._classes.DecisionTreeClassifier(1)_max_depth	null
TEST0b9e0b5718sklearn.tree._classes.DecisionTreeClassifier(1)_max_features	null
TEST0b9e0b5718sklearn.tree._classes.DecisionTreeClassifier(1)_max_leaf_nodes	null
TEST0b9e0b5718sklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_decrease	0.0
TEST0b9e0b5718sklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_split	null
TEST0b9e0b5718sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_leaf	1
TEST0b9e0b5718sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_split	2
TEST0b9e0b5718sklearn.tree._classes.DecisionTreeClassifier(1)_min_weight_fraction_leaf	0.0
TEST0b9e0b5718sklearn.tree._classes.DecisionTreeClassifier(1)_random_state	62501
TEST0b9e0b5718sklearn.tree._classes.DecisionTreeClassifier(1)_splitter	"best"