| 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 | 17583 |
| sklearn.svm.classes.SVC(46)_shrinking | true |
| sklearn.svm.classes.SVC(46)_tol | 0.001 |
| sklearn.svm.classes.SVC(46)_verbose | false |
0.9295 ± 0.034 Per class Cross-validation details (10-fold Crossvalidation)
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0.8785 ± 0.032 Per class Cross-validation details (10-fold Crossvalidation)
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0.7261 ± 0.0727 Cross-validation details (10-fold Crossvalidation)
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0.605 ± 0.0486 Cross-validation details (10-fold Crossvalidation)
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0.1859 ± 0.0192 Cross-validation details (10-fold Crossvalidation)
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0.4472 ± 0.0012 Cross-validation details (10-fold Crossvalidation)
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0.8796 ± 0.0316 Cross-validation details (10-fold Crossvalidation)
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1055 Per class Cross-validation details (10-fold Crossvalidation) |
0.8785 ± 0.0329 Per class Cross-validation details (10-fold Crossvalidation)
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0.8796 ± 0.0316 Cross-validation details (10-fold Crossvalidation)
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0.9223 ± 0.0036 Cross-validation details (10-fold Crossvalidation)
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0.4156 ± 0.043 Cross-validation details (10-fold Crossvalidation)
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0.4728 ± 0.0013 Cross-validation details (10-fold Crossvalidation)
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0.3018 ± 0.0324 Cross-validation details (10-fold Crossvalidation)
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0.6382 ± 0.0686 Cross-validation details (10-fold Crossvalidation)
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0.8568 ± 0.0385 Cross-validation details (10-fold Crossvalidation)
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