Run
10559805

Run 10559805

Task 10101 (Supervised Classification) blood-transfusion-service-center Uploaded 10-03-2021 by Fabrice Normandin
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Flow

sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.Standar dScaler,svc=sklearn.svm.classes.SVC)(7)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.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(7)_memorynull
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(7)_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)(7)_verbosefalse
sklearn.preprocessing.data.StandardScaler(41)_copytrue
sklearn.preprocessing.data.StandardScaler(41)_with_meantrue
sklearn.preprocessing.data.StandardScaler(41)_with_stdtrue
sklearn.svm.classes.SVC(43)_C503.87021587258033
sklearn.svm.classes.SVC(43)_cache_size200
sklearn.svm.classes.SVC(43)_class_weightnull
sklearn.svm.classes.SVC(43)_coef00.0
sklearn.svm.classes.SVC(43)_decision_function_shape"ovr"
sklearn.svm.classes.SVC(43)_degree4
sklearn.svm.classes.SVC(43)_gamma783.8379520702346
sklearn.svm.classes.SVC(43)_kernel"sigmoid"
sklearn.svm.classes.SVC(43)_max_iter-1
sklearn.svm.classes.SVC(43)_probabilityfalse
sklearn.svm.classes.SVC(43)_random_state11344
sklearn.svm.classes.SVC(43)_shrinkingtrue
sklearn.svm.classes.SVC(43)_tol0.001
sklearn.svm.classes.SVC(43)_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

0.5326 ± 0.0617
Per class
Cross-validation details (10-fold Crossvalidation)
0.6628 ± 0.0504
Per class
Cross-validation details (10-fold Crossvalidation)
0.0657 ± 0.1275
Cross-validation details (10-fold Crossvalidation)
-0.0429 ± 0.1795
Cross-validation details (10-fold Crossvalidation)
0.3356 ± 0.0569
Cross-validation details (10-fold Crossvalidation)
0.363 ± 0.0023
Cross-validation details (10-fold Crossvalidation)
0.6644 ± 0.0569
Cross-validation details (10-fold Crossvalidation)
748
Per class
Cross-validation details (10-fold Crossvalidation)
0.6612 ± 0.0477
Per class
Cross-validation details (10-fold Crossvalidation)
0.6644 ± 0.0569
Cross-validation details (10-fold Crossvalidation)
0.7916 ± 0.0072
Cross-validation details (10-fold Crossvalidation)
0.9243 ± 0.1583
Cross-validation details (10-fold Crossvalidation)
0.4258 ± 0.0027
Cross-validation details (10-fold Crossvalidation)
0.5793 ± 0.0496
Cross-validation details (10-fold Crossvalidation)
1.3603 ± 0.1189
Cross-validation details (10-fold Crossvalidation)
0.5326 ± 0.0617
Cross-validation details (10-fold Crossvalidation)