Run 10592291

Task 168794 (Supervised Classification) robert Uploaded 22-03-2023 by Yinuo Guo
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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estima tor=sklearn.svm._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.impute._base.SimpleImputer(42)_add_indicator	false
sklearn.impute._base.SimpleImputer(42)_copy	true
sklearn.impute._base.SimpleImputer(42)_fill_value	null
sklearn.impute._base.SimpleImputer(42)_keep_empty_features	false
sklearn.impute._base.SimpleImputer(42)_missing_values	NaN
sklearn.impute._base.SimpleImputer(42)_strategy	"mean"
sklearn.impute._base.SimpleImputer(42)_verbose	"deprecated"
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.svm._classes.SVC)(1)_memory	null
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.svm._classes.SVC)(1)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": "estimator"}}]
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.svm._classes.SVC)(1)_verbose	false
sklearn.svm._classes.SVC(14)_C	1.0
sklearn.svm._classes.SVC(14)_break_ties	false
sklearn.svm._classes.SVC(14)_cache_size	200
sklearn.svm._classes.SVC(14)_class_weight	null
sklearn.svm._classes.SVC(14)_coef0	0.0
sklearn.svm._classes.SVC(14)_decision_function_shape	"ovr"
sklearn.svm._classes.SVC(14)_degree	3
sklearn.svm._classes.SVC(14)_gamma	"scale"
sklearn.svm._classes.SVC(14)_kernel	"rbf"
sklearn.svm._classes.SVC(14)_max_iter	-1
sklearn.svm._classes.SVC(14)_probability	false
sklearn.svm._classes.SVC(14)_random_state	50441
sklearn.svm._classes.SVC(14)_shrinking	true
sklearn.svm._classes.SVC(14)_tol	0.001
sklearn.svm._classes.SVC(14)_verbose	false