| Issue | #Downvotes for this reason | By |
|---|
| base_estimator | sklearn.tree.tree.DecisionTreeClassifier(56) | The base estimator from which the boosted ensemble is built Support for sample weighting is required, as well as proper ``classes_`` and ``n_classes_`` attributes. If ``None``, then the base estimator is ``DecisionTreeClassifier(max_depth=1)`` |
| algorithm | default: "SAMME.R" | |
| base_estimator | The base estimator from which the boosted ensemble is built Support for sample weighting is required, as well as proper ``classes_`` and ``n_classes_`` attributes. If ``None``, then the base estimator is ``DecisionTreeClassifier(max_depth=1)`` | default: {"oml-python:serialized_object": "component_reference", "value": {"key": "base_estimator", "step_name": null}} |
| learning_rate | Learning rate shrinks the contribution of each classifier by ``learning_rate``. There is a trade-off between ``learning_rate`` and ``n_estimators`` algorithm : {'SAMME', 'SAMME.R'}, optional (default='SAMME.R') If 'SAMME.R' then use the SAMME.R real boosting algorithm ``base_estimator`` must support calculation of class probabilities If 'SAMME' then use the SAMME discrete boosting algorithm The SAMME.R algorithm typically converges faster than SAMME, achieving a lower test error with fewer boosting iterations | default: 1.0 |
| n_estimators | The maximum number of estimators at which boosting is terminated In case of perfect fit, the learning procedure is stopped early | default: 50 |
| random_state | If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. | default: 0 |