Run
10437971

Run 10437971

Task 146822 (Supervised Classification) segment 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_state44794
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

0.9787 ± 0.0069
Per class
Cross-validation details (10-fold Crossvalidation)
0.8899 ± 0.0352
Per class
Cross-validation details (10-fold Crossvalidation)
0.8722 ± 0.0407
Cross-validation details (10-fold Crossvalidation)
0.8212 ± 0.0241
Cross-validation details (10-fold Crossvalidation)
0.0657 ± 0.0053
Cross-validation details (10-fold Crossvalidation)
0.2449
Cross-validation details (10-fold Crossvalidation)
0.8905 ± 0.0349
Cross-validation details (10-fold Crossvalidation)
2310
Per class
Cross-validation details (10-fold Crossvalidation)
0.8905 ± 0.036
Per class
Cross-validation details (10-fold Crossvalidation)
0.8905 ± 0.0349
Cross-validation details (10-fold Crossvalidation)
2.8074
Cross-validation details (10-fold Crossvalidation)
0.2685 ± 0.0216
Cross-validation details (10-fold Crossvalidation)
0.3499
Cross-validation details (10-fold Crossvalidation)
0.1645 ± 0.0178
Cross-validation details (10-fold Crossvalidation)
0.4702 ± 0.0508
Cross-validation details (10-fold Crossvalidation)
0.8905 ± 0.0349
Cross-validation details (10-fold Crossvalidation)