| Issue | #Downvotes for this reason | By |
|---|
| sklearn.pipeline.Pipeline(imputer=sklearn.impute.SimpleImputer,estimator=sk learn.svm.classes.SVC)(2) | 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 transformers in the pipeline can be cached using ``memory`` argument. 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.pipeline.Pipeline(imputer=sklearn.impute.SimpleImputer,estimator=sklearn.svm.classes.SVC)(2)_memory | null |
| sklearn.pipeline.Pipeline(imputer=sklearn.impute.SimpleImputer,estimator=sklearn.svm.classes.SVC)(2)_steps | [{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": "estimator"}}] |
| sklearn.impute.SimpleImputer(20)_copy | true |
| sklearn.impute.SimpleImputer(20)_fill_value | null |
| sklearn.impute.SimpleImputer(20)_missing_values | NaN |
| sklearn.impute.SimpleImputer(20)_strategy | "mean" |
| sklearn.impute.SimpleImputer(20)_verbose | 0 |
| sklearn.svm.classes.SVC(48)_C | 1.0 |
| sklearn.svm.classes.SVC(48)_cache_size | 200 |
| sklearn.svm.classes.SVC(48)_class_weight | null |
| sklearn.svm.classes.SVC(48)_coef0 | 0.0 |
| sklearn.svm.classes.SVC(48)_decision_function_shape | "ovr" |
| sklearn.svm.classes.SVC(48)_degree | 3 |
| sklearn.svm.classes.SVC(48)_gamma | "auto" |
| sklearn.svm.classes.SVC(48)_kernel | "rbf" |
| sklearn.svm.classes.SVC(48)_max_iter | -1 |
| sklearn.svm.classes.SVC(48)_probability | false |
| sklearn.svm.classes.SVC(48)_random_state | 3051 |
| sklearn.svm.classes.SVC(48)_shrinking | true |
| sklearn.svm.classes.SVC(48)_tol | 0.001 |
| sklearn.svm.classes.SVC(48)_verbose | false |
0.8153 ± 0.0504 Per class Cross-validation details (10-fold Crossvalidation)
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0.8457 ± 0.0389 Per class Cross-validation details (10-fold Crossvalidation)
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0.6502 ± 0.0896 Cross-validation details (10-fold Crossvalidation)
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0.6448 ± 0.0828 Cross-validation details (10-fold Crossvalidation)
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0.1517 ± 0.0355 Cross-validation details (10-fold Crossvalidation)
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0.4472 ± 0.0012 Cross-validation details (10-fold Crossvalidation)
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0.8483 ± 0.0355 Cross-validation details (10-fold Crossvalidation)
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1055 Per class Cross-validation details (10-fold Crossvalidation) |
0.8463 ± 0.0355 Per class Cross-validation details (10-fold Crossvalidation)
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0.8483 ± 0.0355 Cross-validation details (10-fold Crossvalidation)
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0.9223 ± 0.0036 Cross-validation details (10-fold Crossvalidation)
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0.3391 ± 0.0791 Cross-validation details (10-fold Crossvalidation)
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0.4728 ± 0.0013 Cross-validation details (10-fold Crossvalidation)
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0.3894 ± 0.0459 Cross-validation details (10-fold Crossvalidation)
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0.8236 ± 0.0962 Cross-validation details (10-fold Crossvalidation)
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0.8153 ± 0.0504 Cross-validation details (10-fold Crossvalidation)
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