| 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 | 45759 |
| sklearn.svm.classes.SVC(46)_shrinking | true |
| sklearn.svm.classes.SVC(46)_tol | 0.001 |
| sklearn.svm.classes.SVC(46)_verbose | false |
0.9102 ± 0.0105 Per class Cross-validation details (10-fold Crossvalidation)
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0.8407 ± 0.0161 Per class Cross-validation details (10-fold Crossvalidation)
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0.6118 ± 0.0398 Cross-validation details (10-fold Crossvalidation)
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0.4884 ± 0.0247 Cross-validation details (10-fold Crossvalidation)
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0.2145 ± 0.0082 Cross-validation details (10-fold Crossvalidation)
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0.4147 ± 0.0003 Cross-validation details (10-fold Crossvalidation)
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0.8425 ± 0.0156 Cross-validation details (10-fold Crossvalidation)
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5404 Per class Cross-validation details (10-fold Crossvalidation) |
0.8397 ± 0.0161 Per class Cross-validation details (10-fold Crossvalidation)
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0.8425 ± 0.0156 Cross-validation details (10-fold Crossvalidation)
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0.8732 ± 0.001 Cross-validation details (10-fold Crossvalidation)
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0.5171 ± 0.0198 Cross-validation details (10-fold Crossvalidation)
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0.4554 ± 0.0004 Cross-validation details (10-fold Crossvalidation)
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0.3295 ± 0.0122 Cross-validation details (10-fold Crossvalidation)
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0.7237 ± 0.0268 Cross-validation details (10-fold Crossvalidation)
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0.7992 ± 0.0218 Cross-validation details (10-fold Crossvalidation)
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