A random subgroups classifier.
A random subgroups classifier is a meta estimator that fits a number
subgroups on various sub-samples of the dataset.
The sub-sample size is controlled with the `max_samples` parameter if
`bootstrap=True` (default), otherwise the whole dataset is used to search
for each subgroup.
The Parameters
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n_estimators : int, default=100
The number of subgroups in the ensemble.
max_depth : int, default=1
The maximum depth of the subgroup discovery task.
max_features : {"auto", "sqrt", "log2"}, int or float, default="auto"
The number of features to consider when looking for the best subgroup:
- If int, then consider `max_features` features for each subgroup.
- If float, then `max_features` is a fraction and
`round(max_features * n_features)` features are considered for each
subgroup.
- If "auto", then `max_features=sqrt(n_features)`.
- If "sqrt", then `max_features=sqrt(n_features)` (same as "auto").
- If "log2", then `max_feature...