Run 10592618

Task 3492 (Supervised Classification) monks-problems-1 Uploaded 23-03-2023 by Takeaki Sakabe
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads

Issue	#Downvotes for this reason	By

Flow

sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estima tor=sklearn.linear_model._ridge.RidgeClassifier)(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(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.linear_model._ridge.RidgeClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.linear_model._ridge.RidgeClassifier)(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.linear_model._ridge.RidgeClassifier)(1)_verbose	false
sklearn.linear_model._ridge.RidgeClassifier(2)_alpha	1.0
sklearn.linear_model._ridge.RidgeClassifier(2)_class_weight	null
sklearn.linear_model._ridge.RidgeClassifier(2)_copy_X	true
sklearn.linear_model._ridge.RidgeClassifier(2)_fit_intercept	true
sklearn.linear_model._ridge.RidgeClassifier(2)_max_iter	null
sklearn.linear_model._ridge.RidgeClassifier(2)_positive	false
sklearn.linear_model._ridge.RidgeClassifier(2)_random_state	2000
sklearn.linear_model._ridge.RidgeClassifier(2)_solver	"auto"
sklearn.linear_model._ridge.RidgeClassifier(2)_tol	0.0001