Run
10559585

Run 10559585

Task 14969 (Supervised Classification) GesturePhaseSegmentationProcessed Uploaded 13-08-2020 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)_C126.01836378200282
sklearn.svm.classes.SVC(40)_cache_size200
sklearn.svm.classes.SVC(40)_class_weightnull
sklearn.svm.classes.SVC(40)_coef0-0.29389563974752275
sklearn.svm.classes.SVC(40)_decision_function_shape"ovr"
sklearn.svm.classes.SVC(40)_degree2
sklearn.svm.classes.SVC(40)_gamma0.1324591300630764
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

0.821 ± 0.0056
Per class
Cross-validation details (10-fold Crossvalidation)
0.5434 ± 0.0083
Per class
Cross-validation details (10-fold Crossvalidation)
0.4219 ± 0.0113
Cross-validation details (10-fold Crossvalidation)
0.325 ± 0.0066
Cross-validation details (10-fold Crossvalidation)
0.2415 ± 0.0022
Cross-validation details (10-fold Crossvalidation)
0.3065 ± 0
Cross-validation details (10-fold Crossvalidation)
0.5744 ± 0.0082
Cross-validation details (10-fold Crossvalidation)
9873
Per class
Cross-validation details (10-fold Crossvalidation)
0.5721 ± 0.0113
Per class
Cross-validation details (10-fold Crossvalidation)
0.5744 ± 0.0082
Cross-validation details (10-fold Crossvalidation)
2.1934 ± 0.0008
Cross-validation details (10-fold Crossvalidation)
0.788 ± 0.007
Cross-validation details (10-fold Crossvalidation)
0.3915 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
0.3406 ± 0.0023
Cross-validation details (10-fold Crossvalidation)
0.8702 ± 0.0059
Cross-validation details (10-fold Crossvalidation)
0.472 ± 0.0081
Cross-validation details (10-fold Crossvalidation)