Run
10464789

Run 10464789

Task 9983 (Supervised Classification) eeg-eye-state Uploaded 22-05-2020 by Marc Zöller
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  • automl_meta_features openml-python Sklearn_0.22.1.
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Flow

sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.Normalizer,ste p_1=sklearn.svm._classes.SVC)(1)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.svm._classes.SVC(4)_C7.314167875704607
sklearn.svm._classes.SVC(4)_break_tiesfalse
sklearn.svm._classes.SVC(4)_cache_size200
sklearn.svm._classes.SVC(4)_class_weightnull
sklearn.svm._classes.SVC(4)_coef00.0
sklearn.svm._classes.SVC(4)_decision_function_shape"ovo"
sklearn.svm._classes.SVC(4)_degree3
sklearn.svm._classes.SVC(4)_gamma"scale"
sklearn.svm._classes.SVC(4)_kernel"linear"
sklearn.svm._classes.SVC(4)_max_iter-1
sklearn.svm._classes.SVC(4)_probabilityfalse
sklearn.svm._classes.SVC(4)_random_state42
sklearn.svm._classes.SVC(4)_shrinkingfalse
sklearn.svm._classes.SVC(4)_tol2.571147542101562e-06
sklearn.svm._classes.SVC(4)_verbosefalse
sklearn.preprocessing._data.Normalizer(1)_copyfalse
sklearn.preprocessing._data.Normalizer(1)_norm"max"
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.Normalizer,step_1=sklearn.svm._classes.SVC)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.Normalizer,step_1=sklearn.svm._classes.SVC)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "step_0", "step_name": "step_0"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_1", "step_name": "step_1"}}]
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.Normalizer,step_1=sklearn.svm._classes.SVC)(1)_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

0.5201 ± 0.0036
Per class
Cross-validation details (10-fold Crossvalidation)
0.4352 ± 0.0062
Per class
Cross-validation details (10-fold Crossvalidation)
0.0442 ± 0.0078
Cross-validation details (10-fold Crossvalidation)
0.1241 ± 0.0066
Cross-validation details (10-fold Crossvalidation)
0.4314 ± 0.0032
Cross-validation details (10-fold Crossvalidation)
0.4948 ± 0
Cross-validation details (10-fold Crossvalidation)
0.5686 ± 0.0032
Cross-validation details (10-fold Crossvalidation)
14980
Per class
Cross-validation details (10-fold Crossvalidation)
0.6934 ± 0.0209
Per class
Cross-validation details (10-fold Crossvalidation)
0.5686 ± 0.0032
Cross-validation details (10-fold Crossvalidation)
0.9924 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
0.8719 ± 0.0065
Cross-validation details (10-fold Crossvalidation)
0.4974 ± 0
Cross-validation details (10-fold Crossvalidation)
0.6568 ± 0.0025
Cross-validation details (10-fold Crossvalidation)
1.3205 ± 0.005
Cross-validation details (10-fold Crossvalidation)
0.5201 ± 0.0036
Cross-validation details (10-fold Crossvalidation)