Run
10559284

Run 10559284

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)_C5.266616263789896
sklearn.svm.classes.SVC(40)_cache_size200
sklearn.svm.classes.SVC(40)_class_weightnull
sklearn.svm.classes.SVC(40)_coef0-0.7111819894510212
sklearn.svm.classes.SVC(40)_decision_function_shape"ovr"
sklearn.svm.classes.SVC(40)_degree2
sklearn.svm.classes.SVC(40)_gamma0.012541197776805249
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

0.835 ± 0.0519
Per class
Cross-validation details (10-fold Crossvalidation)
0.7676 ± 0.0603
Per class
Cross-validation details (10-fold Crossvalidation)
0.4782 ± 0.1376
Cross-validation details (10-fold Crossvalidation)
0.3292 ± 0.0631
Cross-validation details (10-fold Crossvalidation)
0.3125 ± 0.0227
Cross-validation details (10-fold Crossvalidation)
0.4545 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
0.776 ± 0.0524
Cross-validation details (10-fold Crossvalidation)
768
Per class
Cross-validation details (10-fold Crossvalidation)
0.7714 ± 0.0597
Per class
Cross-validation details (10-fold Crossvalidation)
0.776 ± 0.0524
Cross-validation details (10-fold Crossvalidation)
0.9331 ± 0.0032
Cross-validation details (10-fold Crossvalidation)
0.6876 ± 0.0502
Cross-validation details (10-fold Crossvalidation)
0.4766 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
0.3942 ± 0.0303
Cross-validation details (10-fold Crossvalidation)
0.8271 ± 0.0642
Cross-validation details (10-fold Crossvalidation)
0.7259 ± 0.0706
Cross-validation details (10-fold Crossvalidation)