Run 2393

Task 119 (Supervised Classification) diabetes Uploaded 17-10-2024 by Continuous Integration
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TEST017cbdd005sklearn.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`.
TEST017cbdd005sklearn.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
TEST017cbdd005sklearn.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"}}]
TEST017cbdd005sklearn.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
TEST017cbdd005sklearn.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
TEST017cbdd005sklearn.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"
TEST017cbdd005sklearn.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
TEST017cbdd005sklearn.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
TEST017cbdd005sklearn.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": []}}]
TEST017cbdd005sklearn.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
TEST017cbdd005sklearn.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_feature_names_out	true
TEST017cbdd005sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)(1)_memory	null
TEST017cbdd005sklearn.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"}}]
TEST017cbdd005sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)(1)_verbose	false
TEST017cbdd005sklearn.impute._base.SimpleImputer(1)_add_indicator	false
TEST017cbdd005sklearn.impute._base.SimpleImputer(1)_copy	true
TEST017cbdd005sklearn.impute._base.SimpleImputer(1)_fill_value	null
TEST017cbdd005sklearn.impute._base.SimpleImputer(1)_missing_values	NaN
TEST017cbdd005sklearn.impute._base.SimpleImputer(1)_strategy	"mean"
TEST017cbdd005sklearn.impute._base.SimpleImputer(1)_verbose	"deprecated"
TEST017cbdd005sklearn.preprocessing._data.StandardScaler(1)_copy	true
TEST017cbdd005sklearn.preprocessing._data.StandardScaler(1)_with_mean	true
TEST017cbdd005sklearn.preprocessing._data.StandardScaler(1)_with_std	true
TEST017cbdd005sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_memory	null
TEST017cbdd005sklearn.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"}}]
TEST017cbdd005sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_verbose	false
TEST017cbdd005openml.testing.CustomImputer(1)_add_indicator	false
TEST017cbdd005openml.testing.CustomImputer(1)_copy	true
TEST017cbdd005openml.testing.CustomImputer(1)_fill_value	null
TEST017cbdd005openml.testing.CustomImputer(1)_missing_values	NaN
TEST017cbdd005openml.testing.CustomImputer(1)_strategy	"most_frequent"
TEST017cbdd005openml.testing.CustomImputer(1)_verbose	"deprecated"
TEST017cbdd005sklearn.preprocessing._encoders.OneHotEncoder(1)_categories	"auto"
TEST017cbdd005sklearn.preprocessing._encoders.OneHotEncoder(1)_drop	null
TEST017cbdd005sklearn.preprocessing._encoders.OneHotEncoder(1)_dtype	{"oml-python:serialized_object": "type", "value": "np.float64"}
TEST017cbdd005sklearn.preprocessing._encoders.OneHotEncoder(1)_handle_unknown	"ignore"
TEST017cbdd005sklearn.preprocessing._encoders.OneHotEncoder(1)_max_categories	null
TEST017cbdd005sklearn.preprocessing._encoders.OneHotEncoder(1)_min_frequency	null
TEST017cbdd005sklearn.preprocessing._encoders.OneHotEncoder(1)_sparse	true
TEST017cbdd005sklearn.tree._classes.DecisionTreeClassifier(1)_ccp_alpha	0.0
TEST017cbdd005sklearn.tree._classes.DecisionTreeClassifier(1)_class_weight	null
TEST017cbdd005sklearn.tree._classes.DecisionTreeClassifier(1)_criterion	"gini"
TEST017cbdd005sklearn.tree._classes.DecisionTreeClassifier(1)_max_depth	null
TEST017cbdd005sklearn.tree._classes.DecisionTreeClassifier(1)_max_features	null
TEST017cbdd005sklearn.tree._classes.DecisionTreeClassifier(1)_max_leaf_nodes	null
TEST017cbdd005sklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_decrease	0.0
TEST017cbdd005sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_leaf	1
TEST017cbdd005sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_split	2
TEST017cbdd005sklearn.tree._classes.DecisionTreeClassifier(1)_min_weight_fraction_leaf	0.0
TEST017cbdd005sklearn.tree._classes.DecisionTreeClassifier(1)_random_state	62501
TEST017cbdd005sklearn.tree._classes.DecisionTreeClassifier(1)_splitter	"best"