Run
10559586

Run 10559586

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)_C616.7326694603752
sklearn.svm.classes.SVC(40)_cache_size200
sklearn.svm.classes.SVC(40)_class_weightnull
sklearn.svm.classes.SVC(40)_coef0-0.43976701452156863
sklearn.svm.classes.SVC(40)_decision_function_shape"ovr"
sklearn.svm.classes.SVC(40)_degree2
sklearn.svm.classes.SVC(40)_gamma0.0003262760044002536
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

0.7882 ± 0.0059
Per class
Cross-validation details (10-fold Crossvalidation)
0.4794 ± 0.0164
Per class
Cross-validation details (10-fold Crossvalidation)
0.3483 ± 0.0224
Cross-validation details (10-fold Crossvalidation)
0.2846 ± 0.0064
Cross-validation details (10-fold Crossvalidation)
0.2519 ± 0.0018
Cross-validation details (10-fold Crossvalidation)
0.3065 ± 0
Cross-validation details (10-fold Crossvalidation)
0.5193 ± 0.0161
Cross-validation details (10-fold Crossvalidation)
9873
Per class
Cross-validation details (10-fold Crossvalidation)
0.5068 ± 0.0216
Per class
Cross-validation details (10-fold Crossvalidation)
0.5193 ± 0.0161
Cross-validation details (10-fold Crossvalidation)
2.1934 ± 0.0008
Cross-validation details (10-fold Crossvalidation)
0.8219 ± 0.0059
Cross-validation details (10-fold Crossvalidation)
0.3915 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
0.3518 ± 0.0019
Cross-validation details (10-fold Crossvalidation)
0.8987 ± 0.005
Cross-validation details (10-fold Crossvalidation)
0.4209 ± 0.0149
Cross-validation details (10-fold Crossvalidation)