ADWINBaggingClassifier¶
ADWIN Bagging classifier.
ADWIN Bagging 1 is the online bagging 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 bag.
-
n_models β defaults to
10
The number of models in the ensemble.
-
seed (int) β 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 optim
>>> from river import preprocessing
>>> dataset = datasets.Phishing()
>>> model = ensemble.ADWINBaggingClassifier(
... model=(
... preprocessing.StandardScaler() |
... linear_model.LogisticRegression()
... ),
... n_models=3,
... seed=42
... )
>>> metric = metrics.F1()
>>> evaluate.progressive_val_score(dataset, model, metric)
F1: 87.83%
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
Update the model with a set of features x
and a label y
.
Parameters
- x
- y
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 β 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
References¶
-
Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Richard Kirkby, and Ricard GavaldΓ . "New ensemble methods for evolving data streams." In 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2009. ↩
-
Oza, N., Russell, S. "Online bagging and boosting." In: Artificial Intelligence and Statistics 2001, pp. 105β112. Morgan Kaufmann, 2001. ↩