| 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 | 39952 |
| sklearn.svm.classes.SVC(46)_shrinking | true |
| sklearn.svm.classes.SVC(46)_tol | 0.001 |
| sklearn.svm.classes.SVC(46)_verbose | false |
0.7284 ± 0.0359 Per class Cross-validation details (10-fold Crossvalidation)
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0.5352 ± 0.0351 Per class Cross-validation details (10-fold Crossvalidation)
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0.2791 ± 0.0521 Cross-validation details (10-fold Crossvalidation)
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0.2002 ± 0.0245 Cross-validation details (10-fold Crossvalidation)
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0.3748 ± 0.0082 Cross-validation details (10-fold Crossvalidation)
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0.4308 ± 0.0003 Cross-validation details (10-fold Crossvalidation)
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0.5465 ± 0.033 Cross-validation details (10-fold Crossvalidation)
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1473 Per class Cross-validation details (10-fold Crossvalidation) |
0.5377 ± 0.0406 Per class Cross-validation details (10-fold Crossvalidation)
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0.5465 ± 0.033 Cross-validation details (10-fold Crossvalidation)
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1.539 ± 0.0019 Cross-validation details (10-fold Crossvalidation)
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0.87 ± 0.0191 Cross-validation details (10-fold Crossvalidation)
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0.4641 ± 0.0003 Cross-validation details (10-fold Crossvalidation)
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0.4306 ± 0.0109 Cross-validation details (10-fold Crossvalidation)
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0.9277 ± 0.0234 Cross-validation details (10-fold Crossvalidation)
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0.5072 ± 0.037 Cross-validation details (10-fold Crossvalidation)
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