| 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 | 32685 |
| sklearn.svm.classes.SVC(46)_shrinking | true |
| sklearn.svm.classes.SVC(46)_tol | 0.001 |
| sklearn.svm.classes.SVC(46)_verbose | false |
0.9852 ± 0.0038 Per class Cross-validation details (10-fold Crossvalidation)
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0.8869 ± 0.0198 Per class Cross-validation details (10-fold Crossvalidation)
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0.8682 ± 0.0239 Cross-validation details (10-fold Crossvalidation)
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0.8568 ± 0.0146 Cross-validation details (10-fold Crossvalidation)
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0.0518 ± 0.0038 Cross-validation details (10-fold Crossvalidation)
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0.2449 Cross-validation details (10-fold Crossvalidation)
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0.887 ± 0.0205 Cross-validation details (10-fold Crossvalidation)
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2310 Per class Cross-validation details (10-fold Crossvalidation) |
0.8873 ± 0.0204 Per class Cross-validation details (10-fold Crossvalidation)
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0.887 ± 0.0205 Cross-validation details (10-fold Crossvalidation)
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2.8074 Cross-validation details (10-fold Crossvalidation)
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0.2116 ± 0.0156 Cross-validation details (10-fold Crossvalidation)
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0.3499 Cross-validation details (10-fold Crossvalidation)
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0.1561 ± 0.0105 Cross-validation details (10-fold Crossvalidation)
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0.4462 ± 0.0301 Cross-validation details (10-fold Crossvalidation)
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0.887 ± 0.0205 Cross-validation details (10-fold Crossvalidation)
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