| 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 | 24123 |
| sklearn.svm.classes.SVC(46)_shrinking | true |
| sklearn.svm.classes.SVC(46)_tol | 0.001 |
| sklearn.svm.classes.SVC(46)_verbose | false |
0.9242 ± 0.012 Per class Cross-validation details (10-fold Crossvalidation)
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0.761 ± 0.0329 Per class Cross-validation details (10-fold Crossvalidation)
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0.6901 ± 0.043 Cross-validation details (10-fold Crossvalidation)
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0.6726 ± 0.0162 Cross-validation details (10-fold Crossvalidation)
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0.098 ± 0.0043 Cross-validation details (10-fold Crossvalidation)
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0.2223 ± 0.0002 Cross-validation details (10-fold Crossvalidation)
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0.7609 ± 0.0333 Cross-validation details (10-fold Crossvalidation)
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1941 Per class Cross-validation details (10-fold Crossvalidation) |
0.7634 ± 0.0315 Per class Cross-validation details (10-fold Crossvalidation)
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0.7609 ± 0.0333 Cross-validation details (10-fold Crossvalidation)
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2.4107 ± 0.0095 Cross-validation details (10-fold Crossvalidation)
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0.4409 ± 0.0193 Cross-validation details (10-fold Crossvalidation)
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0.3334 ± 0.0003 Cross-validation details (10-fold Crossvalidation)
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0.2194 ± 0.0092 Cross-validation details (10-fold Crossvalidation)
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0.658 ± 0.0274 Cross-validation details (10-fold Crossvalidation)
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0.7654 ± 0.0309 Cross-validation details (10-fold Crossvalidation)
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