Run
10438087

Run 10438087

Task 3918 (Supervised Classification) pc1 Uploaded 31-03-2020 by Nicolas Hug
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  • openml-python Sklearn_0.23.dev0.
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Flow

sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer, standardscaler=sklearn.preprocessing._data.StandardScaler,svc=sklearn.svm._ classes.SVC)(2)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(3)_copytrue
sklearn.preprocessing._data.StandardScaler(3)_with_meantrue
sklearn.preprocessing._data.StandardScaler(3)_with_stdtrue
sklearn.svm._classes.SVC(3)_C1.0
sklearn.svm._classes.SVC(3)_break_tiesfalse
sklearn.svm._classes.SVC(3)_cache_size200
sklearn.svm._classes.SVC(3)_class_weightnull
sklearn.svm._classes.SVC(3)_coef00.0
sklearn.svm._classes.SVC(3)_decision_function_shape"ovr"
sklearn.svm._classes.SVC(3)_degree3
sklearn.svm._classes.SVC(3)_gamma"scale"
sklearn.svm._classes.SVC(3)_kernel"linear"
sklearn.svm._classes.SVC(3)_max_iter-1
sklearn.svm._classes.SVC(3)_probabilitytrue
sklearn.svm._classes.SVC(3)_random_state37939
sklearn.svm._classes.SVC(3)_shrinkingtrue
sklearn.svm._classes.SVC(3)_tol0.001
sklearn.svm._classes.SVC(3)_verbosefalse
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler,svc=sklearn.svm._classes.SVC)(2)_memorynull
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler,svc=sklearn.svm._classes.SVC)(2)_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)(2)_verbosefalse
sklearn.impute._base.SimpleImputer(15)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(15)_copytrue
sklearn.impute._base.SimpleImputer(15)_fill_valuenull
sklearn.impute._base.SimpleImputer(15)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(15)_strategy"mean"
sklearn.impute._base.SimpleImputer(15)_verbose0

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.

16 Evaluation measures

0.6764 ± 0.0638
Per class
Cross-validation details (10-fold Crossvalidation)
-0.0085 ± 0.0428
Cross-validation details (10-fold Crossvalidation)
0.1276 ± 0.0033
Cross-validation details (10-fold Crossvalidation)
0.1299 ± 0.0037
Cross-validation details (10-fold Crossvalidation)
0.9306 ± 0.0043
Cross-validation details (10-fold Crossvalidation)
1109
Per class
Cross-validation details (10-fold Crossvalidation)
0.9306 ± 0.0043
Cross-validation details (10-fold Crossvalidation)
0.3638 ± 0.0159
Cross-validation details (10-fold Crossvalidation)
0.9822 ± 0.0135
Cross-validation details (10-fold Crossvalidation)
0.2542 ± 0.0073
Cross-validation details (10-fold Crossvalidation)
0.2525 ± 0.0079
Cross-validation details (10-fold Crossvalidation)
0.9934 ± 0.0077
Cross-validation details (10-fold Crossvalidation)
0.5
Cross-validation details (10-fold Crossvalidation)