Run 10438079

Task 22 (Supervised Classification) mfeat-zernike Uploaded 31-03-2020 by Nicolas Hug
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sklearn.calibration.CalibratedClassifierCV(base_estimator=sklearn.pipeline. Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sk learn.preprocessing._data.StandardScaler,svc=sklearn.svm._classes.SVC))(1)	Probability calibration with isotonic regression or logistic regression. The calibration is based on the :term:`decision_function` method of the `base_estimator` if it exists, else on :term:`predict_proba`.
sklearn.preprocessing._data.StandardScaler(3)_copy	true
sklearn.preprocessing._data.StandardScaler(3)_with_mean	true
sklearn.preprocessing._data.StandardScaler(3)_with_std	true
sklearn.svm._classes.SVC(3)_C	1.0
sklearn.svm._classes.SVC(3)_break_ties	false
sklearn.svm._classes.SVC(3)_cache_size	200
sklearn.svm._classes.SVC(3)_class_weight	null
sklearn.svm._classes.SVC(3)_coef0	0.0
sklearn.svm._classes.SVC(3)_decision_function_shape	"ovr"
sklearn.svm._classes.SVC(3)_degree	3
sklearn.svm._classes.SVC(3)_gamma	"scale"
sklearn.svm._classes.SVC(3)_kernel	"linear"
sklearn.svm._classes.SVC(3)_max_iter	-1
sklearn.svm._classes.SVC(3)_probability	false
sklearn.svm._classes.SVC(3)_random_state	10985
sklearn.svm._classes.SVC(3)_shrinking	true
sklearn.svm._classes.SVC(3)_tol	0.001
sklearn.svm._classes.SVC(3)_verbose	false
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler,svc=sklearn.svm._classes.SVC)(2)_memory	null
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler,svc=sklearn.svm._classes.SVC)(2)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "svc", "step_name": "svc"}}]
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler,svc=sklearn.svm._classes.SVC)(2)_verbose	false
sklearn.impute._base.SimpleImputer(15)_add_indicator	false
sklearn.impute._base.SimpleImputer(15)_copy	true
sklearn.impute._base.SimpleImputer(15)_fill_value	null
sklearn.impute._base.SimpleImputer(15)_missing_values	NaN
sklearn.impute._base.SimpleImputer(15)_strategy	"mean"
sklearn.impute._base.SimpleImputer(15)_verbose	0
sklearn.calibration.CalibratedClassifierCV(base_estimator=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler,svc=sklearn.svm._classes.SVC))(1)_cv	5
sklearn.calibration.CalibratedClassifierCV(base_estimator=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler,svc=sklearn.svm._classes.SVC))(1)_method	"sigmoid"