| 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 | 15974 |
| sklearn.svm.classes.SVC(46)_shrinking | true |
| sklearn.svm.classes.SVC(46)_tol | 0.001 |
| sklearn.svm.classes.SVC(46)_verbose | false |
0.9242 ± 0.0119 Per class Cross-validation details (10-fold Crossvalidation)
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0.7619 ± 0.0336 Per class Cross-validation details (10-fold Crossvalidation)
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0.6911 ± 0.0441 Cross-validation details (10-fold Crossvalidation)
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0.6733 ± 0.0165 Cross-validation details (10-fold Crossvalidation)
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0.0977 ± 0.0045 Cross-validation details (10-fold Crossvalidation)
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0.2223 ± 0.0002 Cross-validation details (10-fold Crossvalidation)
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0.762 ± 0.0339 Cross-validation details (10-fold Crossvalidation)
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1941 Per class Cross-validation details (10-fold Crossvalidation) |
0.7647 ± 0.0312 Per class Cross-validation details (10-fold Crossvalidation)
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0.762 ± 0.0339 Cross-validation details (10-fold Crossvalidation)
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2.4107 ± 0.0095 Cross-validation details (10-fold Crossvalidation)
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0.4395 ± 0.02 Cross-validation details (10-fold Crossvalidation)
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0.3334 ± 0.0003 Cross-validation details (10-fold Crossvalidation)
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0.2193 ± 0.0095 Cross-validation details (10-fold Crossvalidation)
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0.6578 ± 0.0282 Cross-validation details (10-fold Crossvalidation)
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0.7661 ± 0.0321 Cross-validation details (10-fold Crossvalidation)
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