| 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 | 52651 |
| sklearn.svm.classes.SVC(46)_shrinking | true |
| sklearn.svm.classes.SVC(46)_tol | 0.001 |
| sklearn.svm.classes.SVC(46)_verbose | false |
0.995 ± 0.0087 Per class Cross-validation details (10-fold Crossvalidation)
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0.9737 ± 0.0239 Per class Cross-validation details (10-fold Crossvalidation)
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0.9437 ± 0.0514 Cross-validation details (10-fold Crossvalidation)
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0.908 ± 0.043 Cross-validation details (10-fold Crossvalidation)
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0.0466 ± 0.019 Cross-validation details (10-fold Crossvalidation)
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0.4676 ± 0.0019 Cross-validation details (10-fold Crossvalidation)
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0.9736 ± 0.0237 Cross-validation details (10-fold Crossvalidation)
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569 Per class Cross-validation details (10-fold Crossvalidation) |
0.9737 ± 0.0233 Per class Cross-validation details (10-fold Crossvalidation)
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0.9736 ± 0.0237 Cross-validation details (10-fold Crossvalidation)
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0.9526 ± 0.0055 Cross-validation details (10-fold Crossvalidation)
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0.0997 ± 0.0406 Cross-validation details (10-fold Crossvalidation)
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0.4835 ± 0.0019 Cross-validation details (10-fold Crossvalidation)
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0.143 ± 0.0533 Cross-validation details (10-fold Crossvalidation)
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0.2958 ± 0.1101 Cross-validation details (10-fold Crossvalidation)
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0.9723 ± 0.0278 Cross-validation details (10-fold Crossvalidation)
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