# BaggingClassifier¶

Online bootstrap aggregation for classification.

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) to obtain more detailed values.

## Parameters¶

• model (base.Classifier)

The classifier 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.

• 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.Phishing()

>>> model = ensemble.BaggingClassifier(
...     model=(
...         preprocessing.StandardScaler() |
...         linear_model.LogisticRegression()
...     ),
...     n_models=3,
...     seed=42
... )

>>> metric = metrics.F1()

>>> evaluate.progressive_val_score(dataset, model, metric)
F1: 87.83%

>>> print(model)
BaggingClassifier(StandardScaler | LogisticRegression)


## Methods¶

append

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

Parameters

• item
clear

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

clone

Return a fresh estimator with the same parameters.

The clone has the same parameters but has not been updated with any data. This works by looking at the parameters from the class signature. Each parameter is either - recursively cloned if it's a River classes. - deep-copied via copy.deepcopy if not. If the calling object is stochastic (i.e. it accepts a seed parameter) and has not been seeded, then the clone will not be idempotent. Indeed, this method's purpose if simply to return a new instance with the same input parameters.

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

Predict the label of a set of features x.

Parameters

• x (dict)

Returns

typing.Union[bool, str, int]: The predicted label.

predict_proba_one

Averages the predictions of each classifier.

Parameters

• x
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