Run
10438046

Run 10438046

Task 146824 (Supervised Classification) mfeat-pixel Uploaded 31-03-2020 by Nicolas Hug
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  • openml-python Sklearn_0.23.dev0.
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Flow

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)_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_state41725
sklearn.svm._classes.SVC(3)_shrinkingtrue
sklearn.svm._classes.SVC(3)_tol0.001
sklearn.svm._classes.SVC(3)_verbosefalse
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler,svc=sklearn.svm._classes.SVC)(2)_memorynull
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)_verbosefalse
sklearn.impute._base.SimpleImputer(15)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(15)_copytrue
sklearn.impute._base.SimpleImputer(15)_fill_valuenull
sklearn.impute._base.SimpleImputer(15)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(15)_strategy"mean"
sklearn.impute._base.SimpleImputer(15)_verbose0
sklearn.calibration.CalibratedClassifierCV(base_estimator=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler,svc=sklearn.svm._classes.SVC))(1)_cv5
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"

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

Area under ROC curve
0.9941 ± 0.0034
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.976 ± 0.0081
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.9733 ± 0.009
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.8959 ± 0.0065
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0387 ± 0.0015
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.18
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.976 ± 0.0081
Cross-validation details (10-fold Crossvalidation)
Number of instances
2000
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9762 ± 0.0076
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.976 ± 0.0081
Cross-validation details (10-fold Crossvalidation)
Prior entropy
3.3219
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.2153 ± 0.0086
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.0977 ± 0.0053
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.3257 ± 0.0176
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.976 ± 0.0081
Cross-validation details (10-fold Crossvalidation)