| Issue | #Downvotes for this reason | By |
|---|
| sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_sele ction.SelectPercentile,step_1=sklearn.ensemble._weight_boosting.AdaBoostCla ssifier)(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" |
| sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_base_estimator | null |
| sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_learning_rate | 2.916757066690216e-06 |
| sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_n_estimators | 537 |
| sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_random_state | 42 |
| sklearn.feature_selection._univariate_selection.SelectPercentile(1)_percentile | 28.13678768024938 |
| sklearn.feature_selection._univariate_selection.SelectPercentile(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.SelectPercentile,step_1=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(1)_memory | null |
| sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.SelectPercentile,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.SelectPercentile,step_1=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(1)_verbose | false |
0.5536 ± 0.0132 Per class Cross-validation details (10-fold Crossvalidation)
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0.5217 ± 0.027 Per class Cross-validation details (10-fold Crossvalidation)
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0.1168 ± 0.0262 Cross-validation details (10-fold Crossvalidation)
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0.0886 ± 0.0087 Cross-validation details (10-fold Crossvalidation)
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0.4571 ± 0.0036 Cross-validation details (10-fold Crossvalidation)
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0.4948 ± 0 Cross-validation details (10-fold Crossvalidation)
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0.5928 ± 0.0078 Cross-validation details (10-fold Crossvalidation)
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14980 Per class Cross-validation details (10-fold Crossvalidation) |
0.6231 ± 0.0149 Per class Cross-validation details (10-fold Crossvalidation)
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0.5928 ± 0.0078 Cross-validation details (10-fold Crossvalidation)
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0.9924 ± 0.0001 Cross-validation details (10-fold Crossvalidation)
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0.9239 ± 0.0072 Cross-validation details (10-fold Crossvalidation)
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0.4974 ± 0 Cross-validation details (10-fold Crossvalidation)
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0.5104 ± 0.0035 Cross-validation details (10-fold Crossvalidation)
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1.0262 ± 0.0071 Cross-validation details (10-fold Crossvalidation)
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0.5544 ± 0.0132 Cross-validation details (10-fold Crossvalidation)
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