| 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 | 18422 |
| sklearn.svm.classes.SVC(46)_shrinking | true |
| sklearn.svm.classes.SVC(46)_tol | 0.001 |
| sklearn.svm.classes.SVC(46)_verbose | false |
0.6955 ± 0.0592 Per class Cross-validation details (10-fold Crossvalidation)
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0.7161 ± 0.0514 Per class Cross-validation details (10-fold Crossvalidation)
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0.1545 ± 0.1494 Cross-validation details (10-fold Crossvalidation)
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0.0979 ± 0.0471 Cross-validation details (10-fold Crossvalidation)
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0.3292 ± 0.0109 Cross-validation details (10-fold Crossvalidation)
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0.363 ± 0.0023 Cross-validation details (10-fold Crossvalidation)
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0.7727 ± 0.037 Cross-validation details (10-fold Crossvalidation)
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748 Per class Cross-validation details (10-fold Crossvalidation) |
0.7381 ± 0.0959 Per class Cross-validation details (10-fold Crossvalidation)
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0.7727 ± 0.037 Cross-validation details (10-fold Crossvalidation)
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0.7916 ± 0.0072 Cross-validation details (10-fold Crossvalidation)
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0.9067 ± 0.0282 Cross-validation details (10-fold Crossvalidation)
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0.4258 ± 0.0027 Cross-validation details (10-fold Crossvalidation)
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0.4023 ± 0.0179 Cross-validation details (10-fold Crossvalidation)
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0.9448 ± 0.0416 Cross-validation details (10-fold Crossvalidation)
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0.5572 ± 0.0586 Cross-validation details (10-fold Crossvalidation)
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