| Issue | #Downvotes for this reason | By |
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| sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estima tor=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(3) | 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.impute._base.SimpleImputer(55)_add_indicator | false |
| sklearn.impute._base.SimpleImputer(55)_copy | true |
| sklearn.impute._base.SimpleImputer(55)_fill_value | null |
| sklearn.impute._base.SimpleImputer(55)_keep_empty_features | false |
| sklearn.impute._base.SimpleImputer(55)_missing_values | NaN |
| sklearn.impute._base.SimpleImputer(55)_strategy | "mean" |
| sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(3)_memory | null |
| sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(3)_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.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(3)_verbose | false |
| sklearn.ensemble._weight_boosting.AdaBoostClassifier(8)_algorithm | "SAMME.R" |
| sklearn.ensemble._weight_boosting.AdaBoostClassifier(8)_estimator | null |
| sklearn.ensemble._weight_boosting.AdaBoostClassifier(8)_learning_rate | 1.0 |
| sklearn.ensemble._weight_boosting.AdaBoostClassifier(8)_n_estimators | 50 |
| sklearn.ensemble._weight_boosting.AdaBoostClassifier(8)_random_state | 48148 |
0.8711 ± 0.0303 Per class Cross-validation details (10-fold Crossvalidation)
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0.8698 ± 0.0175 Per class Cross-validation details (10-fold Crossvalidation)
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0.4149 ± 0.0824 Cross-validation details (10-fold Crossvalidation)
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-2.1455 ± 0.0157 Cross-validation details (10-fold Crossvalidation)
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0.485 ± 0.0012 Cross-validation details (10-fold Crossvalidation)
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0.2429 ± 0.0007 Cross-validation details (10-fold Crossvalidation)
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0.8842 ± 0.0145 Cross-validation details (10-fold Crossvalidation)
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5000 Per class Cross-validation details (10-fold Crossvalidation) |
0.8691 ± 0.021 Per class Cross-validation details (10-fold Crossvalidation)
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0.8842 ± 0.0145 Cross-validation details (10-fold Crossvalidation)
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0.5879 ± 0.0025 Cross-validation details (10-fold Crossvalidation)
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1.9965 ± 0.007 Cross-validation details (10-fold Crossvalidation)
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0.3484 ± 0.001 Cross-validation details (10-fold Crossvalidation)
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0.4854 ± 0.0011 Cross-validation details (10-fold Crossvalidation)
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1.3932 ± 0.0047 Cross-validation details (10-fold Crossvalidation)
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0.6691 ± 0.0352 Cross-validation details (10-fold Crossvalidation)
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