Run
10560795

Run 10560795

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)_C10
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)_gamma0.01
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_state8377
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

0.8873 ± 0.0677
Per class
Cross-validation details (10-fold Crossvalidation)
0.8892 ± 0.0659
Per class
Cross-validation details (10-fold Crossvalidation)
0.7771 ± 0.132
Cross-validation details (10-fold Crossvalidation)
0.7774 ± 0.1305
Cross-validation details (10-fold Crossvalidation)
0.1106 ± 0.0648
Cross-validation details (10-fold Crossvalidation)
0.4978 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
0.8894 ± 0.0648
Cross-validation details (10-fold Crossvalidation)
208
Per class
Cross-validation details (10-fold Crossvalidation)
0.89 ± 0.0623
Per class
Cross-validation details (10-fold Crossvalidation)
0.8894 ± 0.0648
Cross-validation details (10-fold Crossvalidation)
0.9967 ± 0.0033
Cross-validation details (10-fold Crossvalidation)
0.2222 ± 0.1302
Cross-validation details (10-fold Crossvalidation)
0.4989 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
0.3325 ± 0.1336
Cross-validation details (10-fold Crossvalidation)
0.6666 ± 0.2677
Cross-validation details (10-fold Crossvalidation)
0.8873 ± 0.0677
Cross-validation details (10-fold Crossvalidation)