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.poisson(1).pmf(k) to obtain more detailed values.
The classifier to bag.
n_models – defaults to
The number of models in the ensemble.
seed (int) – defaults to
Random number generator seed for reproducibility.
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: 0.877788 >>> print(model) BaggingClassifier(StandardScaler | LogisticRegression)
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.
Predict the label of a set of features
- x (dict)
typing.Union[bool, str, int]: The predicted label.
Averages the predictions of each classifier.