Run
10559319

Run 10559319

Task 37 (Supervised Classification) diabetes Uploaded 12-08-2020 by Heinrich Peters
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Flow

sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer, standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.cl asses.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.preprocessing.data.StandardScaler(35)_copytrue
sklearn.preprocessing.data.StandardScaler(35)_with_meantrue
sklearn.preprocessing.data.StandardScaler(35)_with_stdtrue
sklearn.impute._base.SimpleImputer(11)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(11)_copytrue
sklearn.impute._base.SimpleImputer(11)_fill_valuenull
sklearn.impute._base.SimpleImputer(11)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(11)_strategy"median"
sklearn.impute._base.SimpleImputer(11)_verbose0
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(4)_memorynull
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(4)_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)(4)_verbosefalse
sklearn.svm.classes.SVC(40)_C15.690273823876444
sklearn.svm.classes.SVC(40)_cache_size200
sklearn.svm.classes.SVC(40)_class_weightnull
sklearn.svm.classes.SVC(40)_coef00.30581799832036705
sklearn.svm.classes.SVC(40)_decision_function_shape"ovr"
sklearn.svm.classes.SVC(40)_degree2
sklearn.svm.classes.SVC(40)_gamma1.0669788592014713
sklearn.svm.classes.SVC(40)_kernel"rbf"
sklearn.svm.classes.SVC(40)_max_iter-1
sklearn.svm.classes.SVC(40)_probabilitytrue
sklearn.svm.classes.SVC(40)_random_state1
sklearn.svm.classes.SVC(40)_shrinkingtrue
sklearn.svm.classes.SVC(40)_tol0.001
sklearn.svm.classes.SVC(40)_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

0.722 ± 0.0465
Per class
Cross-validation details (10-fold Crossvalidation)
0.643 ± 0.0607
Per class
Cross-validation details (10-fold Crossvalidation)
0.1955 ± 0.1346
Cross-validation details (10-fold Crossvalidation)
0.132 ± 0.0464
Cross-validation details (10-fold Crossvalidation)
0.3938 ± 0.0167
Cross-validation details (10-fold Crossvalidation)
0.4545 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
0.6693 ± 0.0516
Cross-validation details (10-fold Crossvalidation)
768
Per class
Cross-validation details (10-fold Crossvalidation)
0.6467 ± 0.0641
Per class
Cross-validation details (10-fold Crossvalidation)
0.6693 ± 0.0516
Cross-validation details (10-fold Crossvalidation)
0.9331 ± 0.0032
Cross-validation details (10-fold Crossvalidation)
0.8664 ± 0.0363
Cross-validation details (10-fold Crossvalidation)
0.4766 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
0.4496 ± 0.0179
Cross-validation details (10-fold Crossvalidation)
0.9432 ± 0.0368
Cross-validation details (10-fold Crossvalidation)
0.5884 ± 0.0634
Cross-validation details (10-fold Crossvalidation)