| Issue | #Downvotes for this reason | By |
|---|
| sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.Normalizer,ste p_1=sklearn.ensemble._weight_boosting.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 | 0.0002798359368788071 |
| sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_n_estimators | 910 |
| sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_random_state | 42 |
| sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.Normalizer,step_1=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(1)_memory | null |
| sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.Normalizer,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.preprocessing._data.Normalizer,step_1=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(1)_verbose | false |
| sklearn.preprocessing._data.Normalizer(1)_copy | false |
| sklearn.preprocessing._data.Normalizer(1)_norm | "max" |
0.885 ± 0.0082 Per class Cross-validation details (10-fold Crossvalidation)
|
0.6788 ± 0.0373 Per class Cross-validation details (10-fold Crossvalidation)
|
0.6135 ± 0.0459 Cross-validation details (10-fold Crossvalidation)
|
0.4324 ± 0.0277 Cross-validation details (10-fold Crossvalidation)
|
0.1437 ± 0.0058 Cross-validation details (10-fold Crossvalidation)
|
0.1975 Cross-validation details (10-fold Crossvalidation)
|
0.6565 ± 0.0408 Cross-validation details (10-fold Crossvalidation)
|
1080 Per class Cross-validation details (10-fold Crossvalidation) |
0.8344 ± 0.0158 Per class Cross-validation details (10-fold Crossvalidation)
|
0.6565 ± 0.0408 Cross-validation details (10-fold Crossvalidation)
|
3.1699 Cross-validation details (10-fold Crossvalidation)
|
0.7275 ± 0.0292 Cross-validation details (10-fold Crossvalidation)
|
0.3143 Cross-validation details (10-fold Crossvalidation)
|
0.2536 ± 0.0059 Cross-validation details (10-fold Crossvalidation)
|
0.8068 ± 0.0187 Cross-validation details (10-fold Crossvalidation)
|
0.6565 ± 0.0408 Cross-validation details (10-fold Crossvalidation)
|