Run
10559297

Run 10559297

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)_C0.04763431960619049
sklearn.svm.classes.SVC(40)_cache_size200
sklearn.svm.classes.SVC(40)_class_weightnull
sklearn.svm.classes.SVC(40)_coef0-0.21633948699433825
sklearn.svm.classes.SVC(40)_decision_function_shape"ovr"
sklearn.svm.classes.SVC(40)_degree1
sklearn.svm.classes.SVC(40)_gamma2.0076472291150194
sklearn.svm.classes.SVC(40)_kernel"poly"
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

0.8264 ± 0.0569
Per class
Cross-validation details (10-fold Crossvalidation)
0.7618 ± 0.0559
Per class
Cross-validation details (10-fold Crossvalidation)
0.4651 ± 0.1285
Cross-validation details (10-fold Crossvalidation)
0.3165 ± 0.057
Cross-validation details (10-fold Crossvalidation)
0.3183 ± 0.0195
Cross-validation details (10-fold Crossvalidation)
0.4545 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
0.7708 ± 0.0511
Cross-validation details (10-fold Crossvalidation)
768
Per class
Cross-validation details (10-fold Crossvalidation)
0.7658 ± 0.0582
Per class
Cross-validation details (10-fold Crossvalidation)
0.7708 ± 0.0511
Cross-validation details (10-fold Crossvalidation)
0.9331 ± 0.0032
Cross-validation details (10-fold Crossvalidation)
0.7004 ± 0.0432
Cross-validation details (10-fold Crossvalidation)
0.4766 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
0.3982 ± 0.0316
Cross-validation details (10-fold Crossvalidation)
0.8353 ± 0.067
Cross-validation details (10-fold Crossvalidation)
0.7193 ± 0.0657
Cross-validation details (10-fold Crossvalidation)