Run
10560796

Run 10560796

Task 39 (Supervised Classification) sonar Uploaded 01-09-2021 by Victorien Fandos
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Flow

sklearn.pipeline.Pipeline(StandardScaler=sklearn.preprocessing._data.Standa rdScaler,svc=sklearn.svm._classes.SVC)(4)Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit and transform methods. The final estimator only needs to implement fit. The transformers in the pipeline can be cached using ``memory`` argument. The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a '__', as in the example below. A step's estimator may be replaced entirely by setting the parameter with its name to another estimator, or a transformer removed by setting it to 'passthrough' or ``None``.
sklearn.svm._classes.SVC(9)_C4
sklearn.svm._classes.SVC(9)_break_tiesfalse
sklearn.svm._classes.SVC(9)_cache_size200
sklearn.svm._classes.SVC(9)_class_weightnull
sklearn.svm._classes.SVC(9)_coef00.0
sklearn.svm._classes.SVC(9)_decision_function_shape"ovr"
sklearn.svm._classes.SVC(9)_degree3
sklearn.svm._classes.SVC(9)_gamma0.03
sklearn.svm._classes.SVC(9)_kernel"rbf"
sklearn.svm._classes.SVC(9)_max_iter-1
sklearn.svm._classes.SVC(9)_probabilityfalse
sklearn.svm._classes.SVC(9)_random_state29470
sklearn.svm._classes.SVC(9)_shrinkingtrue
sklearn.svm._classes.SVC(9)_tol0.001
sklearn.svm._classes.SVC(9)_verbosefalse
sklearn.preprocessing._data.StandardScaler(9)_copytrue
sklearn.preprocessing._data.StandardScaler(9)_with_meantrue
sklearn.preprocessing._data.StandardScaler(9)_with_stdtrue
sklearn.pipeline.Pipeline(StandardScaler=sklearn.preprocessing._data.StandardScaler,svc=sklearn.svm._classes.SVC)(4)_memorynull
sklearn.pipeline.Pipeline(StandardScaler=sklearn.preprocessing._data.StandardScaler,svc=sklearn.svm._classes.SVC)(4)_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)(4)_verbosefalse

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

0.8647 ± 0.0434
Per class
Cross-validation details (10-fold Crossvalidation)
0.8689 ± 0.0428
Per class
Cross-validation details (10-fold Crossvalidation)
0.7366 ± 0.0839
Cross-validation details (10-fold Crossvalidation)
0.7387 ± 0.0816
Cross-validation details (10-fold Crossvalidation)
0.1298 ± 0.0405
Cross-validation details (10-fold Crossvalidation)
0.4978 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
0.8702 ± 0.0405
Cross-validation details (10-fold Crossvalidation)
208
Per class
Cross-validation details (10-fold Crossvalidation)
0.8769 ± 0.0348
Per class
Cross-validation details (10-fold Crossvalidation)
0.8702 ± 0.0405
Cross-validation details (10-fold Crossvalidation)
0.9967 ± 0.0033
Cross-validation details (10-fold Crossvalidation)
0.2608 ± 0.0814
Cross-validation details (10-fold Crossvalidation)
0.4989 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
0.3603 ± 0.0546
Cross-validation details (10-fold Crossvalidation)
0.7222 ± 0.1093
Cross-validation details (10-fold Crossvalidation)
0.8647 ± 0.0434
Cross-validation details (10-fold Crossvalidation)