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Task 119 (Supervised Classification) diabetes Uploaded 11-01-2024 by Continuous Integration
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TESTa936885df6sklearn.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``.
TESTa936885df6sklearn.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
TESTa936885df6sklearn.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"}}]
TESTa936885df6sklearn.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
TESTa936885df6sklearn.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
TESTa936885df6sklearn.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"
TESTa936885df6sklearn.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
TESTa936885df6sklearn.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
TESTa936885df6sklearn.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": []}}]
TESTa936885df6sklearn.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
TESTa936885df6sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)(1)_memory	null
TESTa936885df6sklearn.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"}}]
TESTa936885df6sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)(1)_verbose	false
TESTa936885df6sklearn.impute._base.SimpleImputer(1)_add_indicator	false
TESTa936885df6sklearn.impute._base.SimpleImputer(1)_copy	true
TESTa936885df6sklearn.impute._base.SimpleImputer(1)_fill_value	null
TESTa936885df6sklearn.impute._base.SimpleImputer(1)_missing_values	NaN
TESTa936885df6sklearn.impute._base.SimpleImputer(1)_strategy	"mean"
TESTa936885df6sklearn.impute._base.SimpleImputer(1)_verbose	0
TESTa936885df6sklearn.preprocessing._data.StandardScaler(1)_copy	true
TESTa936885df6sklearn.preprocessing._data.StandardScaler(1)_with_mean	true
TESTa936885df6sklearn.preprocessing._data.StandardScaler(1)_with_std	true
TESTa936885df6sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_memory	null
TESTa936885df6sklearn.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"}}]
TESTa936885df6sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_verbose	false
TESTa936885df6openml.testing.CustomImputer(1)_add_indicator	false
TESTa936885df6openml.testing.CustomImputer(1)_copy	true
TESTa936885df6openml.testing.CustomImputer(1)_fill_value	null
TESTa936885df6openml.testing.CustomImputer(1)_missing_values	NaN
TESTa936885df6openml.testing.CustomImputer(1)_strategy	"most_frequent"
TESTa936885df6openml.testing.CustomImputer(1)_verbose	0
TESTa936885df6sklearn.preprocessing._encoders.OneHotEncoder(1)_categories	"auto"
TESTa936885df6sklearn.preprocessing._encoders.OneHotEncoder(1)_drop	null
TESTa936885df6sklearn.preprocessing._encoders.OneHotEncoder(1)_dtype	{"oml-python:serialized_object": "type", "value": "np.float64"}
TESTa936885df6sklearn.preprocessing._encoders.OneHotEncoder(1)_handle_unknown	"ignore"
TESTa936885df6sklearn.preprocessing._encoders.OneHotEncoder(1)_sparse	true
TESTa936885df6sklearn.tree._classes.DecisionTreeClassifier(1)_ccp_alpha	0.0
TESTa936885df6sklearn.tree._classes.DecisionTreeClassifier(1)_class_weight	null
TESTa936885df6sklearn.tree._classes.DecisionTreeClassifier(1)_criterion	"gini"
TESTa936885df6sklearn.tree._classes.DecisionTreeClassifier(1)_max_depth	null
TESTa936885df6sklearn.tree._classes.DecisionTreeClassifier(1)_max_features	null
TESTa936885df6sklearn.tree._classes.DecisionTreeClassifier(1)_max_leaf_nodes	null
TESTa936885df6sklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_decrease	0.0
TESTa936885df6sklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_split	null
TESTa936885df6sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_leaf	1
TESTa936885df6sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_split	2
TESTa936885df6sklearn.tree._classes.DecisionTreeClassifier(1)_min_weight_fraction_leaf	0.0
TESTa936885df6sklearn.tree._classes.DecisionTreeClassifier(1)_random_state	62501
TESTa936885df6sklearn.tree._classes.DecisionTreeClassifier(1)_splitter	"best"