Run
10437960

Run 10437960

Task 11 (Supervised Classification) balance-scale Uploaded 31-03-2020 by Nicolas Hug
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  • openml-python Sklearn_0.23.dev0.
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sklearn.calibration.CalibratedClassifierCV(base_estimator=sklearn.pipeline. Pipeline(standardscaler=sklearn.preprocessing._data.StandardScaler,svc=skle arn.svm._classes.SVC))(2)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.pipeline.Pipeline(standardscaler=sklearn.preprocessing._data.StandardScaler,svc=sklearn.svm._classes.SVC)(2)_memorynull
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing._data.StandardScaler,svc=sklearn.svm._classes.SVC)(2)_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)(2)_verbosefalse
sklearn.preprocessing._data.StandardScaler(3)_copytrue
sklearn.preprocessing._data.StandardScaler(3)_with_meantrue
sklearn.preprocessing._data.StandardScaler(3)_with_stdtrue
sklearn.svm._classes.SVC(3)_C1.0
sklearn.svm._classes.SVC(3)_break_tiesfalse
sklearn.svm._classes.SVC(3)_cache_size200
sklearn.svm._classes.SVC(3)_class_weightnull
sklearn.svm._classes.SVC(3)_coef00.0
sklearn.svm._classes.SVC(3)_decision_function_shape"ovr"
sklearn.svm._classes.SVC(3)_degree3
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)_probabilityfalse
sklearn.svm._classes.SVC(3)_random_state46697
sklearn.svm._classes.SVC(3)_shrinkingtrue
sklearn.svm._classes.SVC(3)_tol0.001
sklearn.svm._classes.SVC(3)_verbosefalse
sklearn.calibration.CalibratedClassifierCV(base_estimator=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing._data.StandardScaler,svc=sklearn.svm._classes.SVC))(2)_cv5
sklearn.calibration.CalibratedClassifierCV(base_estimator=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing._data.StandardScaler,svc=sklearn.svm._classes.SVC))(2)_method"sigmoid"

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

18 Evaluation measures

0.9731 ± 0.0119
Per class
Cross-validation details (10-fold Crossvalidation)
0.9148 ± 0.0278
Per class
Cross-validation details (10-fold Crossvalidation)
0.8419 ± 0.0514
Cross-validation details (10-fold Crossvalidation)
0.7668 ± 0.0434
Cross-validation details (10-fold Crossvalidation)
0.1051 ± 0.0132
Cross-validation details (10-fold Crossvalidation)
0.3798 ± 0.0012
Cross-validation details (10-fold Crossvalidation)
0.9072 ± 0.0311
Cross-validation details (10-fold Crossvalidation)
625
Per class
Cross-validation details (10-fold Crossvalidation)
0.9289 ± 0.0274
Per class
Cross-validation details (10-fold Crossvalidation)
0.9072 ± 0.0311
Cross-validation details (10-fold Crossvalidation)
1.3181 ± 0.0124
Cross-validation details (10-fold Crossvalidation)
0.2767 ± 0.0344
Cross-validation details (10-fold Crossvalidation)
0.4356 ± 0.0014
Cross-validation details (10-fold Crossvalidation)
0.2077 ± 0.0309
Cross-validation details (10-fold Crossvalidation)
0.4768 ± 0.0705
Cross-validation details (10-fold Crossvalidation)
0.8764 ± 0.0783
Cross-validation details (10-fold Crossvalidation)