| 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 | 55116 |
| sklearn.svm.classes.SVC(46)_shrinking | true |
| sklearn.svm.classes.SVC(46)_tol | 0.001 |
| sklearn.svm.classes.SVC(46)_verbose | false |
0.6997 ± 0.0592 Per class Cross-validation details (10-fold Crossvalidation)
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0.7223 ± 0.0547 Per class Cross-validation details (10-fold Crossvalidation)
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0.173 ± 0.1598 Cross-validation details (10-fold Crossvalidation)
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0.1042 ± 0.0538 Cross-validation details (10-fold Crossvalidation)
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0.3267 ± 0.0128 Cross-validation details (10-fold Crossvalidation)
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0.363 ± 0.0023 Cross-validation details (10-fold Crossvalidation)
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0.7754 ± 0.039 Cross-validation details (10-fold Crossvalidation)
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748 Per class Cross-validation details (10-fold Crossvalidation) |
0.7436 ± 0.0972 Per class Cross-validation details (10-fold Crossvalidation)
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0.7754 ± 0.039 Cross-validation details (10-fold Crossvalidation)
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0.7916 ± 0.0072 Cross-validation details (10-fold Crossvalidation)
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0.9 ± 0.0345 Cross-validation details (10-fold Crossvalidation)
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0.4258 ± 0.0027 Cross-validation details (10-fold Crossvalidation)
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0.4016 ± 0.019 Cross-validation details (10-fold Crossvalidation)
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0.943 ± 0.0441 Cross-validation details (10-fold Crossvalidation)
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0.5648 ± 0.0626 Cross-validation details (10-fold Crossvalidation)
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