Leveraging Bagging ensemble classifier.
Leveraging Bagging [^1] is an improvement over the Oza Bagging algorithm. The bagging performance is leveraged by increasing the re-sampling. It uses a poisson distribution to simulate the re-sampling process. To increase re-sampling it uses a higher
w value of the Poisson distribution (agerage number of events), 6 by default, increasing the input space diversity, by attributing a different range of weights to the data samples.
To deal with concept drift, Leveraging Bagging uses the ADWIN algorithm to monitor the performance of each member of the enemble 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.
Type → base.Classifier
The classifier to bag.
Type → int
The number of models in the ensemble.
Type → float
Indicates the average number of events. This is the lambda parameter of the Poisson distribution used to compute the re-sampling weight.
Type → float
The delta parameter for the ADWIN change detector.
Type → str
The bagging method to use. Can be one of the following:
* 'bag' - Leveraging Bagging using ADWIN.
* 'me' - Assigns \(weight=1\) if sample is misclassified, otherwise \(weight=error/(1-error)\).
* 'half' - Use resampling without replacement for half of the instances.
* 'wt' - Resample without taking out all instances.
* 'subag' - Resampling without replacement.
Type → int | None
Random number generator seed for reproducibility.
Valid bagging_method options.
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.LeveragingBaggingClassifier( model=( preprocessing.StandardScaler() | linear_model.LogisticRegression() ), n_models=3, seed=42 ) metric = metrics.F1() evaluate.progressive_val_score(dataset, model, metric)
Update the model with a set of features
x and a label
Predict the label of a set of features
- x — 'dict'
base.typing.ClfTarget | None: The predicted label.
Averages the predictions of each classifier.