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
Type → base.Classifier
The classifier to bag.
-
n_models
Default →
10The number of models in the ensemble.
-
seed
Type → int | None
Default →
NoneRandom number generator seed for reproducibility.
Attributes¶
- 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: 87.65%
print(model)
BaggingClassifier(StandardScaler | LogisticRegression)
Methods¶
learn_one
predict_one
Predict the label of a set of features x.
Parameters
- x — 'dict'
- kwargs
Returns
base.typing.ClfTarget | None: The predicted label.
predict_proba_one
Averages the predictions of each classifier.
Parameters
- x
- kwargs