| Issue | #Downvotes for this reason | By |
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| sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.on e_hot_encoding.OneHotEncoderComponent,step_1=sklearn.ensemble._weight_boost ing.AdaBoostClassifier)(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 | 0.23627005624633687 |
| sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_n_estimators | 281 |
| sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_random_state | 42 |
| sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(1)_memory | null |
| sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,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=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(1)_verbose | false |
0.9271 ± 0.0159 Per class Cross-validation details (10-fold Crossvalidation)
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0.8269 ± 0.0254 Per class Cross-validation details (10-fold Crossvalidation)
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0.6293 ± 0.0564 Cross-validation details (10-fold Crossvalidation)
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0.3566 ± 0.0291 Cross-validation details (10-fold Crossvalidation)
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0.1792 ± 0.0072 Cross-validation details (10-fold Crossvalidation)
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0.229 ± 0.0006 Cross-validation details (10-fold Crossvalidation)
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0.8223 ± 0.0274 Cross-validation details (10-fold Crossvalidation)
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1728 Per class Cross-validation details (10-fold Crossvalidation) |
0.8344 ± 0.0234 Per class Cross-validation details (10-fold Crossvalidation)
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0.8223 ± 0.0274 Cross-validation details (10-fold Crossvalidation)
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1.2058 ± 0.0088 Cross-validation details (10-fold Crossvalidation)
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0.7824 ± 0.0322 Cross-validation details (10-fold Crossvalidation)
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0.3381 ± 0.0008 Cross-validation details (10-fold Crossvalidation)
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0.2734 ± 0.0089 Cross-validation details (10-fold Crossvalidation)
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0.8085 ± 0.0275 Cross-validation details (10-fold Crossvalidation)
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0.7789 ± 0.0862 Cross-validation details (10-fold Crossvalidation)
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