| 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 | 54835 |
| sklearn.svm.classes.SVC(46)_shrinking | true |
| sklearn.svm.classes.SVC(46)_tol | 0.001 |
| sklearn.svm.classes.SVC(46)_verbose | false |
0.6961 ± 0.0592 Per class Cross-validation details (10-fold Crossvalidation)
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0.7283 ± 0.0476 Per class Cross-validation details (10-fold Crossvalidation)
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0.1912 ± 0.139 Cross-validation details (10-fold Crossvalidation)
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0.1093 ± 0.0598 Cross-validation details (10-fold Crossvalidation)
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0.325 ± 0.0141 Cross-validation details (10-fold Crossvalidation)
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0.363 ± 0.0023 Cross-validation details (10-fold Crossvalidation)
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0.7781 ± 0.0401 Cross-validation details (10-fold Crossvalidation)
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748 Per class Cross-validation details (10-fold Crossvalidation) |
0.7486 ± 0.082 Per class Cross-validation details (10-fold Crossvalidation)
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0.7781 ± 0.0401 Cross-validation details (10-fold Crossvalidation)
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0.7916 ± 0.0072 Cross-validation details (10-fold Crossvalidation)
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0.8951 ± 0.0403 Cross-validation details (10-fold Crossvalidation)
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0.4258 ± 0.0027 Cross-validation details (10-fold Crossvalidation)
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0.4004 ± 0.0205 Cross-validation details (10-fold Crossvalidation)
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0.9402 ± 0.0482 Cross-validation details (10-fold Crossvalidation)
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0.5723 ± 0.0533 Cross-validation details (10-fold Crossvalidation)
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