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BaggingRegressor

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 (base.Regressor)

    The regressor to bag.

  • n_models – defaults to 10

    The number of models in the ensemble.

  • seed (int) – defaults to None

    Random number generator seed for reproducibility.

Attributes

  • models

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: 0.68886

Methods

append

S.append(value) -- append value to the end of the sequence

Parameters

  • item
clear

S.clear() -> None -- remove all items from S

copy
count

S.count(value) -> integer -- return number of occurrences of value

Parameters

  • item
extend

S.extend(iterable) -- extend sequence by appending elements from the iterable

Parameters

  • other
index

S.index(value, [start, [stop]]) -> integer -- return first index of value. Raises ValueError if the value is not present.

Supporting start and stop arguments is optional, but recommended.

Parameters

  • item
  • args
insert

S.insert(index, value) -- insert value before index

Parameters

  • i
  • item
learn_one
pop

S.pop([index]) -> item -- remove and return item at index (default last). Raise IndexError if list is empty or index is out of range.

Parameters

  • i – defaults to -1
predict_one

Averages the predictions of each regressor.

Parameters

  • x
  • kwargs
remove

S.remove(value) -- remove first occurrence of value. Raise ValueError if the value is not present.

Parameters

  • item
reverse

S.reverse() -- reverse IN PLACE

sort

References