Skip to content

ADWINBoostingClassifier

ADWIN Boosting classifier.

ADWIN Boosting 1 is the online boosting method of Oza and Russell 2 with the addition of the ADWIN algorithm as a change detector. If concept drift is detected, the worst member of the ensemble (based on the error estimation by ADWIN) is replaced by a new (empty) classifier.

Parameters

  • model (base.Classifier)

    The classifier to boost.

  • 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

>>> from river import datasets
>>> from river import ensemble
>>> from river import evaluate
>>> from river import linear_model
>>> from river import metrics
>>> from river import preprocessing

>>> dataset = datasets.Phishing()
>>> model = ensemble.ADWINBoostingClassifier(
...     model=(
...         preprocessing.StandardScaler() |
...         linear_model.LogisticRegression()
...     ),
...     n_models=3,
...     seed=42
... )
>>> metric = metrics.F1()

>>> evaluate.progressive_val_score(dataset, model, metric)
F1: 87.41%

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

Update the model with a set of features x and a label y.

Parameters

  • x
  • y
  • kwargs

Returns

self

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)
  • kwargs

Returns

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

predict_proba_one

Predict the probability of each label for a dictionary of features x.

Parameters

  • x
  • kwargs

Returns

A dictionary that associates a probability which each label.

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

  1. Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Richard Kirkby, and Ricard Gavaldà. "New ensemble methods for evolving data streams." In 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2009. ↩

  2. Oza, N., Russell, S. "Online bagging and boosting." In: Artificial Intelligence and Statistics 2001, pp. 105–112. Morgan Kaufmann, 2001. ↩