| Issue | #Downvotes for this reason | By |
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| sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler ,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.preprocessing._data.StandardScaler(20)_copy | true |
| sklearn.preprocessing._data.StandardScaler(20)_with_mean | true |
| sklearn.preprocessing._data.StandardScaler(20)_with_std | true |
| sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,classifier=sklearn.svm._classes.SVC)(1)_memory | null |
| sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,classifier=sklearn.svm._classes.SVC)(1)_steps | [{"oml-python:serialized_object": "component_reference", "value": {"key": "scaler", "step_name": "scaler"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "classifier", "step_name": "classifier"}}] |
| sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,classifier=sklearn.svm._classes.SVC)(1)_verbose | false |
| sklearn.svm._classes.SVC(17)_C | 1 |
| 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 | "linear" |
| 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 |
0.97 ± 0.035 Per class Cross-validation details (10-fold Crossvalidation)
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0.9599 ± 0.048 Per class Cross-validation details (10-fold Crossvalidation)
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0.94 ± 0.0699 Cross-validation details (10-fold Crossvalidation)
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0.9452 ± 0.0638 Cross-validation details (10-fold Crossvalidation)
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0.0267 ± 0.0311 Cross-validation details (10-fold Crossvalidation)
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0.4444 Cross-validation details (10-fold Crossvalidation)
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0.96 ± 0.0466 Cross-validation details (10-fold Crossvalidation)
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150 Per class Cross-validation details (10-fold Crossvalidation) |
0.9619 ± 0.0355 Per class Cross-validation details (10-fold Crossvalidation)
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0.96 ± 0.0466 Cross-validation details (10-fold Crossvalidation)
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1.585 Cross-validation details (10-fold Crossvalidation)
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0.06 ± 0.0699 Cross-validation details (10-fold Crossvalidation)
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0.4714 Cross-validation details (10-fold Crossvalidation)
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0.1633 ± 0.1231 Cross-validation details (10-fold Crossvalidation)
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0.3464 ± 0.2611 Cross-validation details (10-fold Crossvalidation)
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0.96 ± 0.0466 Cross-validation details (10-fold Crossvalidation)
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