Run 10578243

Task 146607 (Supervised Classification) SpeedDating Uploaded 01-12-2021 by Marc Boel
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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)(2)	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`.
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),decisiontreeclassifier=sklearn.tree._classes.DecisionTreeClassifier)(2)_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)(2)_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)(2)_verbose	false
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(2)_n_jobs	null
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(2)_remainder	"drop"
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(2)_sparse_threshold	0.3
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(2)_transformer_weights	null
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(2)_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)(2)_verbose	false
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(2)_verbose_feature_names_out	true
sklearn.impute._base.SimpleImputer(28)_add_indicator	false
sklearn.impute._base.SimpleImputer(28)_copy	true
sklearn.impute._base.SimpleImputer(28)_fill_value	null
sklearn.impute._base.SimpleImputer(28)_missing_values	NaN
sklearn.impute._base.SimpleImputer(28)_strategy	"mean"
sklearn.impute._base.SimpleImputer(28)_verbose	0
sklearn.preprocessing._encoders.OneHotEncoder(30)_categories	"auto"
sklearn.preprocessing._encoders.OneHotEncoder(30)_drop	null
sklearn.preprocessing._encoders.OneHotEncoder(30)_dtype	{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(30)_handle_unknown	"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(30)_sparse	true
sklearn.tree._classes.DecisionTreeClassifier(23)_ccp_alpha	0.0
sklearn.tree._classes.DecisionTreeClassifier(23)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(23)_criterion	"gini"
sklearn.tree._classes.DecisionTreeClassifier(23)_max_depth	null
sklearn.tree._classes.DecisionTreeClassifier(23)_max_features	0.10626717793854201
sklearn.tree._classes.DecisionTreeClassifier(23)_max_leaf_nodes	null
sklearn.tree._classes.DecisionTreeClassifier(23)_min_impurity_decrease	0.0
sklearn.tree._classes.DecisionTreeClassifier(23)_min_samples_leaf	11
sklearn.tree._classes.DecisionTreeClassifier(23)_min_samples_split	16
sklearn.tree._classes.DecisionTreeClassifier(23)_min_weight_fraction_leaf	0.0
sklearn.tree._classes.DecisionTreeClassifier(23)_random_state	60973
sklearn.tree._classes.DecisionTreeClassifier(23)_splitter	"best"