class_docstr: Online bootstrap aggregation for regression.
  
  For each incoming observation, each model's `learn_one` method is called `k` times where
  `k` is sampled from a Poisson distribution of parameter 1. `k` thus has a 36% chance of
  being equal to 0, a 36% chance of being equal to 1, an 18% chance of being equal to 2, a 6%
  chance of being equal to 3, a 1% chance of being equal to 4, etc. You can do
  `scipy.stats.utils.random.poisson(1).pmf(k)` for more detailed values.
  
  Parameters
  ----------
  model
      The regressor to bag.
  n_models
      The number of models in the ensemble.
  seed
      Random number generator seed for reproducibility.
  
  Examples
  --------
  
  In the following example three logistic regressions are bagged together. The performance is
  slightly better than when using a single logistic regression.
  
  >>> from river import datasets
  >>> from river import ensemble
  >>> from river import evaluate
  >>> from river import linear_model
  >>> from river import metrics
  >>> from river import optim
  >>> from river import preprocessing
  
  >>> dataset = datasets.TrumpApproval()
  
  >>> model = preprocessing.StandardScaler()
  >>> model |= ensemble.BaggingRegressor(
  ...     model=linear_model.LinearRegression(intercept_lr=0.1),
  ...     n_models=3,
  ...     seed=42
  ... )
  
  >>> metric = metrics.MAE()
  
  >>> evaluate.progressive_val_score(dataset, model, metric)
  MAE: 0.677586
  
  References
  ----------
  [^1]: [Oza, N.C., 2005, October. Online bagging and boosting. In 2005 IEEE international conference on systems, man and cybernetics (Vol. 3, pp. 2340-2345). Ieee.](https://ti.arc.nasa.gov/m/profile/oza/files/ozru01a.pdf)
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code: def _unit_test_params(cls):
      yield {"model": linear_model.LinearRegression()}
  
imports: from __future__ import annotations
  import collections
  import statistics
  from river import base, drift, linear_model, utils
label_desc: Use image node_id 2 for calling the BaggingRegressor obj's underlying member method code with example usage: obj._unit_test_params(cls) without return types