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Run 10560806

Task 31 (Supervised Classification) credit-g Uploaded 12-09-2021 by Victorien Fandos
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sklearn.pipeline.Pipeline(ColumnTansformer=sklearn.compose._column_transfor mer.ColumnTransformer(OneHotEncoder=sklearn.preprocessing._encoders.OneHotE ncoder),MaxAbsScaler=sklearn.preprocessing._data.MaxAbsScaler,svc=sklearn.s vm._classes.SVC)(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``.
sklearn.preprocessing._encoders.OneHotEncoder(29)_categories	"auto"
sklearn.preprocessing._encoders.OneHotEncoder(29)_drop	null
sklearn.preprocessing._encoders.OneHotEncoder(29)_dtype	{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(29)_handle_unknown	"error"
sklearn.preprocessing._encoders.OneHotEncoder(29)_sparse	true
sklearn.svm._classes.SVC(9)_C	7.5
sklearn.svm._classes.SVC(9)_break_ties	false
sklearn.svm._classes.SVC(9)_cache_size	200
sklearn.svm._classes.SVC(9)_class_weight	null
sklearn.svm._classes.SVC(9)_coef0	0.0
sklearn.svm._classes.SVC(9)_decision_function_shape	"ovr"
sklearn.svm._classes.SVC(9)_degree	3
sklearn.svm._classes.SVC(9)_gamma	0.04
sklearn.svm._classes.SVC(9)_kernel	"poly"
sklearn.svm._classes.SVC(9)_max_iter	-1
sklearn.svm._classes.SVC(9)_probability	false
sklearn.svm._classes.SVC(9)_random_state	63944
sklearn.svm._classes.SVC(9)_shrinking	true
sklearn.svm._classes.SVC(9)_tol	0.001
sklearn.svm._classes.SVC(9)_verbose	false
sklearn.pipeline.Pipeline(ColumnTansformer=sklearn.compose._column_transformer.ColumnTransformer(OneHotEncoder=sklearn.preprocessing._encoders.OneHotEncoder),MaxAbsScaler=sklearn.preprocessing._data.MaxAbsScaler,svc=sklearn.svm._classes.SVC)(1)_memory	null
sklearn.pipeline.Pipeline(ColumnTansformer=sklearn.compose._column_transformer.ColumnTransformer(OneHotEncoder=sklearn.preprocessing._encoders.OneHotEncoder),MaxAbsScaler=sklearn.preprocessing._data.MaxAbsScaler,svc=sklearn.svm._classes.SVC)(1)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "ColumnTansformer", "step_name": "ColumnTansformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "MaxAbsScaler", "step_name": "MaxAbsScaler"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "svc", "step_name": "svc"}}]
sklearn.pipeline.Pipeline(ColumnTansformer=sklearn.compose._column_transformer.ColumnTransformer(OneHotEncoder=sklearn.preprocessing._encoders.OneHotEncoder),MaxAbsScaler=sklearn.preprocessing._data.MaxAbsScaler,svc=sklearn.svm._classes.SVC)(1)_verbose	false
sklearn.compose._column_transformer.ColumnTransformer(OneHotEncoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_n_jobs	null
sklearn.compose._column_transformer.ColumnTransformer(OneHotEncoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_remainder	"passthrough"
sklearn.compose._column_transformer.ColumnTransformer(OneHotEncoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_sparse_threshold	0.3
sklearn.compose._column_transformer.ColumnTransformer(OneHotEncoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_transformer_weights	null
sklearn.compose._column_transformer.ColumnTransformer(OneHotEncoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_transformers	[{"oml-python:serialized_object": "component_reference", "value": {"key": "OneHotEncoder", "step_name": "OneHotEncoder", "argument_1": ["foreign_worker", "own_telephone", "job", "housing", "other_payment_plans", "property_magnitude", "other_parties", "personal_status", "employment", "savings_status", "purpose", "credit_history", "checking_status"]}}]
sklearn.compose._column_transformer.ColumnTransformer(OneHotEncoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_verbose	false
sklearn.preprocessing._data.MaxAbsScaler(2)_copy	true