Run 10593681

Task 361444 (Supervised Classification) phoneme Uploaded 09-05-2023 by Takeaki Sakabe
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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estima tor=sklearn.svm._classes.NuSVC)(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.impute._base.SimpleImputer(43)_add_indicator	false
sklearn.impute._base.SimpleImputer(43)_copy	true
sklearn.impute._base.SimpleImputer(43)_fill_value	null
sklearn.impute._base.SimpleImputer(43)_keep_empty_features	false
sklearn.impute._base.SimpleImputer(43)_missing_values	NaN
sklearn.impute._base.SimpleImputer(43)_strategy	"mean"
sklearn.impute._base.SimpleImputer(43)_verbose	"deprecated"
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.svm._classes.NuSVC)(2)_memory	null
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.svm._classes.NuSVC)(2)_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.NuSVC)(2)_verbose	false
sklearn.svm._classes.NuSVC(4)_break_ties	false
sklearn.svm._classes.NuSVC(4)_cache_size	200
sklearn.svm._classes.NuSVC(4)_class_weight	null
sklearn.svm._classes.NuSVC(4)_coef0	0.0
sklearn.svm._classes.NuSVC(4)_decision_function_shape	"ovr"
sklearn.svm._classes.NuSVC(4)_degree	3
sklearn.svm._classes.NuSVC(4)_gamma	"scale"
sklearn.svm._classes.NuSVC(4)_kernel	"rbf"
sklearn.svm._classes.NuSVC(4)_max_iter	-1
sklearn.svm._classes.NuSVC(4)_nu	0.5
sklearn.svm._classes.NuSVC(4)_probability	false
sklearn.svm._classes.NuSVC(4)_random_state	4335
sklearn.svm._classes.NuSVC(4)_shrinking	true
sklearn.svm._classes.NuSVC(4)_tol	0.001
sklearn.svm._classes.NuSVC(4)_verbose	false