| Issue | #Downvotes for this reason | By |
|---|
| 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" |
| sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_base_estimator | null |
| sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_learning_rate | 1.9939009042971707e-05 |
| sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_n_estimators | 483 |
| 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 |
0.8921 ± 0.0374 Per class Cross-validation details (10-fold Crossvalidation)
|
0.9193 ± 0.0183 Per class Cross-validation details (10-fold Crossvalidation)
|
0.7681 ± 0.0535 Cross-validation details (10-fold Crossvalidation)
|
0.0615 ± 0.0328 Cross-validation details (10-fold Crossvalidation)
|
0.3069 ± 0.0079 Cross-validation details (10-fold Crossvalidation)
|
0.3451 ± 0.0019 Cross-validation details (10-fold Crossvalidation)
|
0.9187 ± 0.0183 Cross-validation details (10-fold Crossvalidation)
|
1156 Per class Cross-validation details (10-fold Crossvalidation) |
0.9203 ± 0.0187 Per class Cross-validation details (10-fold Crossvalidation)
|
0.9187 ± 0.0183 Cross-validation details (10-fold Crossvalidation)
|
0.7628 ± 0.0063 Cross-validation details (10-fold Crossvalidation)
|
0.8893 ± 0.0228 Cross-validation details (10-fold Crossvalidation)
|
0.4152 ± 0.0023 Cross-validation details (10-fold Crossvalidation)
|
0.3318 ± 0.0122 Cross-validation details (10-fold Crossvalidation)
|
0.7991 ± 0.0292 Cross-validation details (10-fold Crossvalidation)
|
0.8905 ± 0.037 Cross-validation details (10-fold Crossvalidation)
|