| Issue | #Downvotes for this reason | By |
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| sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_sele ction.SelectKBest,step_1=sklearn.ensemble._weight_boosting.AdaBoostClassifi er)(1) | 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 it to 'passthrough' or ``None``. |
| sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_algorithm | "SAMME.R" |
| sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_base_estimator | null |
| sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_learning_rate | 0.057117196441063854 |
| sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_n_estimators | 1231 |
| sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_random_state | 42 |
| sklearn.feature_selection._univariate_selection.SelectKBest(1)_k | 1 |
| sklearn.feature_selection._univariate_selection.SelectKBest(1)_score_func | {"oml-python:serialized_object": "function", "value": "sklearn.feature_selection._univariate_selection.chi2"} |
| sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.SelectKBest,step_1=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(1)_memory | null |
| sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.SelectKBest,step_1=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(1)_steps | [{"oml-python:serialized_object": "component_reference", "value": {"key": "step_0", "step_name": "step_0"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_1", "step_name": "step_1"}}] |
| sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.SelectKBest,step_1=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(1)_verbose | false |
0.6386 ± 0.0321 Per class Cross-validation details (10-fold Crossvalidation)
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0.9197 Per class Cross-validation details (10-fold Crossvalidation)
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-0.0004 ± 0.0013 Cross-validation details (10-fold Crossvalidation)
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-9.2868 ± 0.0947 Cross-validation details (10-fold Crossvalidation)
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0.4837 ± 0.0008 Cross-validation details (10-fold Crossvalidation)
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0.1022 ± 0.0006 Cross-validation details (10-fold Crossvalidation)
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0.9459 ± 0.0009 Cross-validation details (10-fold Crossvalidation)
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4839 Per class Cross-validation details (10-fold Crossvalidation) |
0.895 Per class Cross-validation details (10-fold Crossvalidation)
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0.9459 ± 0.0009 Cross-validation details (10-fold Crossvalidation)
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0.3029 ± 0.0027 Cross-validation details (10-fold Crossvalidation)
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4.7318 ± 0.028 Cross-validation details (10-fold Crossvalidation)
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0.2259 ± 0.0013 Cross-validation details (10-fold Crossvalidation)
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0.484 ± 0.0008 Cross-validation details (10-fold Crossvalidation)
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2.1425 ± 0.0125 Cross-validation details (10-fold Crossvalidation)
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0.4999 ± 0.0003 Cross-validation details (10-fold Crossvalidation)
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