Run 10594118

Task 29 (Supervised Classification) credit-approval Uploaded 03-02-2024 by Jano P
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sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transfo rmer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,one hotencoder=sklearn.preprocessing._encoders.OneHotEncoder),decisiontreeclass ifier=sklearn.tree._classes.DecisionTreeClassifier)(9)	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`. For an example use case of `Pipeline` combined with :class:`~sklearn.model_selection.GridSearchCV`, refer to :ref:`sphx_glr_auto_examples_compose_plot_compare_reduction.py`. The example :ref:`sphx_glr_auto_exampl...
sklearn.preprocessing._encoders.OneHotEncoder(50)_categories	"auto"
sklearn.preprocessing._encoders.OneHotEncoder(50)_drop	null
sklearn.preprocessing._encoders.OneHotEncoder(50)_dtype	{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(50)_feature_name_combiner	"concat"
sklearn.preprocessing._encoders.OneHotEncoder(50)_handle_unknown	"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(50)_max_categories	null
sklearn.preprocessing._encoders.OneHotEncoder(50)_min_frequency	null
sklearn.preprocessing._encoders.OneHotEncoder(50)_sparse	"deprecated"
sklearn.preprocessing._encoders.OneHotEncoder(50)_sparse_output	true
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),decisiontreeclassifier=sklearn.tree._classes.DecisionTreeClassifier)(9)_memory	null
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),decisiontreeclassifier=sklearn.tree._classes.DecisionTreeClassifier)(9)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "decisiontreeclassifier", "step_name": "decisiontreeclassifier"}}]
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),decisiontreeclassifier=sklearn.tree._classes.DecisionTreeClassifier)(9)_verbose	false
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(8)_n_jobs	null
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(8)_remainder	"drop"
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(8)_sparse_threshold	0.3
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(8)_transformer_weights	null
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(8)_transformers	[{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cont"}}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cat"}}}]
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(8)_verbose	false
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(8)_verbose_feature_names_out	true
sklearn.impute._base.SimpleImputer(54)_add_indicator	false
sklearn.impute._base.SimpleImputer(54)_copy	true
sklearn.impute._base.SimpleImputer(54)_fill_value	null
sklearn.impute._base.SimpleImputer(54)_keep_empty_features	false
sklearn.impute._base.SimpleImputer(54)_missing_values	NaN
sklearn.impute._base.SimpleImputer(54)_strategy	"mean"
sklearn.tree._classes.DecisionTreeClassifier(45)_ccp_alpha	0.0
sklearn.tree._classes.DecisionTreeClassifier(45)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(45)_criterion	"gini"
sklearn.tree._classes.DecisionTreeClassifier(45)_max_depth	1
sklearn.tree._classes.DecisionTreeClassifier(45)_max_features	null
sklearn.tree._classes.DecisionTreeClassifier(45)_max_leaf_nodes	null
sklearn.tree._classes.DecisionTreeClassifier(45)_min_impurity_decrease	0.0
sklearn.tree._classes.DecisionTreeClassifier(45)_min_samples_leaf	1
sklearn.tree._classes.DecisionTreeClassifier(45)_min_samples_split	2
sklearn.tree._classes.DecisionTreeClassifier(45)_min_weight_fraction_leaf	0.0
sklearn.tree._classes.DecisionTreeClassifier(45)_random_state	21256
sklearn.tree._classes.DecisionTreeClassifier(45)_splitter	"best"