| Issue | #Downvotes for this reason | By |
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| sklearn.pipeline.Pipeline(classifier=sklearn.svm._classes.SVC)(1) | A sequence of data transformers with an optional final predictor. `Pipeline` allows you to sequentially apply a list of transformers to preprocess the data and, if desired, conclude the sequence with a final :term:`predictor` for predictive modeling. Intermediate steps of the pipeline must be 'transforms', that is, they must implement `fit` and `transform` methods. The final :term:`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 it to `'passthrough'` or `None`. For an example use case of `Pipeline` combined with :class:`~s... |
| sklearn.svm._classes.SVC(17)_C | 1.0 |
| sklearn.svm._classes.SVC(17)_break_ties | false |
| sklearn.svm._classes.SVC(17)_cache_size | 200 |
| sklearn.svm._classes.SVC(17)_class_weight | null |
| sklearn.svm._classes.SVC(17)_coef0 | 0.0 |
| sklearn.svm._classes.SVC(17)_decision_function_shape | "ovr" |
| sklearn.svm._classes.SVC(17)_degree | 3 |
| sklearn.svm._classes.SVC(17)_gamma | "scale" |
| sklearn.svm._classes.SVC(17)_kernel | "rbf" |
| sklearn.svm._classes.SVC(17)_max_iter | -1 |
| sklearn.svm._classes.SVC(17)_probability | false |
| sklearn.svm._classes.SVC(17)_random_state | 42 |
| sklearn.svm._classes.SVC(17)_shrinking | true |
| sklearn.svm._classes.SVC(17)_tol | 0.001 |
| sklearn.svm._classes.SVC(17)_verbose | false |
| sklearn.pipeline.Pipeline(classifier=sklearn.svm._classes.SVC)(1)_memory | null |
| sklearn.pipeline.Pipeline(classifier=sklearn.svm._classes.SVC)(1)_steps | [{"oml-python:serialized_object": "component_reference", "value": {"key": "classifier", "step_name": "classifier"}}] |
| sklearn.pipeline.Pipeline(classifier=sklearn.svm._classes.SVC)(1)_verbose | false |
0.5 Per class Cross-validation details (10-fold Crossvalidation)
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0.2433 ± 0.0004 Cross-validation details (10-fold Crossvalidation)
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0.0631 ± 0.0001 Cross-validation details (10-fold Crossvalidation)
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0.1186 ± 0.0001 Cross-validation details (10-fold Crossvalidation)
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0.9369 ± 0.0001 Cross-validation details (10-fold Crossvalidation)
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2534 Per class Cross-validation details (10-fold Crossvalidation) |
0.9369 ± 0.0001 Cross-validation details (10-fold Crossvalidation)
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0.3398 ± 0.0005 Cross-validation details (10-fold Crossvalidation)
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0.5323 ± 0.0006 Cross-validation details (10-fold Crossvalidation)
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0.2432 ± 0.0002 Cross-validation details (10-fold Crossvalidation)
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0.2513 ± 0.0003 Cross-validation details (10-fold Crossvalidation)
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1.0331 ± 0.0001 Cross-validation details (10-fold Crossvalidation)
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0.5 Cross-validation details (10-fold Crossvalidation)
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