| Issue | #Downvotes for this reason | By |
|---|
| sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.Standar dScaler,svc=sklearn.svm.classes.SVC)(8) | 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 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 to None. |
| sklearn.preprocessing.data.StandardScaler(43)_copy | true |
| sklearn.preprocessing.data.StandardScaler(43)_with_mean | true |
| sklearn.preprocessing.data.StandardScaler(43)_with_std | true |
| sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(8)_steps | [{"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.svm.classes.SVC(46)_C | 1.0 |
| sklearn.svm.classes.SVC(46)_cache_size | 200 |
| sklearn.svm.classes.SVC(46)_class_weight | null |
| sklearn.svm.classes.SVC(46)_coef0 | 0.0 |
| sklearn.svm.classes.SVC(46)_decision_function_shape | null |
| sklearn.svm.classes.SVC(46)_degree | 3 |
| sklearn.svm.classes.SVC(46)_gamma | "auto" |
| sklearn.svm.classes.SVC(46)_kernel | "rbf" |
| sklearn.svm.classes.SVC(46)_max_iter | -1 |
| sklearn.svm.classes.SVC(46)_probability | true |
| sklearn.svm.classes.SVC(46)_random_state | 16916 |
| sklearn.svm.classes.SVC(46)_shrinking | true |
| sklearn.svm.classes.SVC(46)_tol | 0.001 |
| sklearn.svm.classes.SVC(46)_verbose | false |
0.9296 ± 0.034 Per class Cross-validation details (10-fold Crossvalidation)
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0.8786 ± 0.0293 Per class Cross-validation details (10-fold Crossvalidation)
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0.7264 ± 0.0665 Cross-validation details (10-fold Crossvalidation)
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0.6072 ± 0.0495 Cross-validation details (10-fold Crossvalidation)
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0.1847 ± 0.0197 Cross-validation details (10-fold Crossvalidation)
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0.4472 ± 0.0012 Cross-validation details (10-fold Crossvalidation)
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0.8796 ± 0.0289 Cross-validation details (10-fold Crossvalidation)
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1055 Per class Cross-validation details (10-fold Crossvalidation) |
0.8785 ± 0.03 Per class Cross-validation details (10-fold Crossvalidation)
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0.8796 ± 0.0289 Cross-validation details (10-fold Crossvalidation)
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0.9223 ± 0.0036 Cross-validation details (10-fold Crossvalidation)
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0.413 ± 0.0441 Cross-validation details (10-fold Crossvalidation)
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0.4728 ± 0.0013 Cross-validation details (10-fold Crossvalidation)
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0.3016 ± 0.0326 Cross-validation details (10-fold Crossvalidation)
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0.6377 ± 0.0689 Cross-validation details (10-fold Crossvalidation)
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0.8575 ± 0.0358 Cross-validation details (10-fold Crossvalidation)
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