Run 10437950

Task 3 (Supervised Classification) kr-vs-kp Uploaded 31-03-2020 by Nicolas Hug
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sklearn.calibration.CalibratedClassifierCV(base_estimator=sklearn.pipeline. Pipeline(standardscaler=sklearn.preprocessing._data.StandardScaler,svc=skle arn.svm._classes.SVC))(1)	Probability calibration with isotonic regression or sigmoid. See glossary entry for :term:`cross-validation estimator`. With this class, the base_estimator is fit on the train set of the cross-validation generator and the test set is used for calibration. The probabilities for each of the folds are then averaged for prediction. In case that cv="prefit" is passed to __init__, it is assumed that base_estimator has been fitted already and all data is used for calibration. Note that data for fitting the classifier and for calibrating it must be disjoint.
sklearn.svm._classes.SVC(2)_C	1.0
sklearn.svm._classes.SVC(2)_break_ties	false
sklearn.svm._classes.SVC(2)_cache_size	200
sklearn.svm._classes.SVC(2)_class_weight	null
sklearn.svm._classes.SVC(2)_coef0	0.0
sklearn.svm._classes.SVC(2)_decision_function_shape	"ovr"
sklearn.svm._classes.SVC(2)_degree	3
sklearn.svm._classes.SVC(2)_gamma	"scale"
sklearn.svm._classes.SVC(2)_kernel	"linear"
sklearn.svm._classes.SVC(2)_max_iter	-1
sklearn.svm._classes.SVC(2)_probability	false
sklearn.svm._classes.SVC(2)_random_state	550
sklearn.svm._classes.SVC(2)_shrinking	true
sklearn.svm._classes.SVC(2)_tol	0.001
sklearn.svm._classes.SVC(2)_verbose	false
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing._data.StandardScaler,svc=sklearn.svm._classes.SVC)(1)_memory	null
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing._data.StandardScaler,svc=sklearn.svm._classes.SVC)(1)_steps	[{"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(standardscaler=sklearn.preprocessing._data.StandardScaler,svc=sklearn.svm._classes.SVC)(1)_verbose	false
sklearn.preprocessing._data.StandardScaler(2)_copy	true
sklearn.preprocessing._data.StandardScaler(2)_with_mean	true
sklearn.preprocessing._data.StandardScaler(2)_with_std	true
sklearn.calibration.CalibratedClassifierCV(base_estimator=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing._data.StandardScaler,svc=sklearn.svm._classes.SVC))(1)_cv	5
sklearn.calibration.CalibratedClassifierCV(base_estimator=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing._data.StandardScaler,svc=sklearn.svm._classes.SVC))(1)_method	"sigmoid"