Run
10559409

Run 10559409

Task 3903 (Supervised Classification) pc3 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)_C33.42207062292044
sklearn.svm.classes.SVC(40)_cache_size200
sklearn.svm.classes.SVC(40)_class_weightnull
sklearn.svm.classes.SVC(40)_coef0-0.8504977207446089
sklearn.svm.classes.SVC(40)_decision_function_shape"ovr"
sklearn.svm.classes.SVC(40)_degree3
sklearn.svm.classes.SVC(40)_gamma1.0382529951745352
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

0.7995 ± 0.0442
Per class
Cross-validation details (10-fold Crossvalidation)
0.8639 ± 0.0161
Per class
Cross-validation details (10-fold Crossvalidation)
0.1177 ± 0.1029
Cross-validation details (10-fold Crossvalidation)
-0.0681 ± 0.1132
Cross-validation details (10-fold Crossvalidation)
0.167 ± 0.0106
Cross-validation details (10-fold Crossvalidation)
0.1842 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
0.8989 ± 0.0138
Cross-validation details (10-fold Crossvalidation)
1563
Per class
Cross-validation details (10-fold Crossvalidation)
0.8673 ± 0.05
Per class
Cross-validation details (10-fold Crossvalidation)
0.8989 ± 0.0138
Cross-validation details (10-fold Crossvalidation)
0.4765 ± 0.001
Cross-validation details (10-fold Crossvalidation)
0.9068 ± 0.0576
Cross-validation details (10-fold Crossvalidation)
0.3031 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
0.2901 ± 0.0175
Cross-validation details (10-fold Crossvalidation)
0.9569 ± 0.0578
Cross-validation details (10-fold Crossvalidation)
0.5367 ± 0.0323
Cross-validation details (10-fold Crossvalidation)