Implementation:Scikit learn contrib Imbalanced learn BalancedBaggingClassifier

Knowledge Sources	imbalanced-learn imbalanced-learn Docs
Domains	Machine_Learning, Ensemble_Learning, Imbalanced_Learning
Last Updated	2026-02-09 03:00 GMT

Overview

Concrete tool for balanced bagging classification provided by the imbalanced-learn library.

Description

The BalancedBaggingClassifier extends sklearn.ensemble.BaggingClassifier with an additional resampling step per bag. The sampler parameter accepts any imblearn sampler (default: RandomUnderSampler). Supports all standard bagging parameters.

Usage

Import this class as a drop-in replacement for BaggingClassifier when balancing is needed. Use the sampler parameter to plug in SMOTE, NearMiss, or any other imblearn sampler.

Code Reference

Source Location

Repository: imbalanced-learn
File: imblearn/ensemble/_bagging.py
Lines: L29-366

Signature

class BalancedBaggingClassifier(BaggingClassifier):
    def __init__(
        self,
        estimator=None,
        n_estimators=10,
        *,
        max_samples=1.0,
        max_features=1.0,
        bootstrap=True,
        bootstrap_features=False,
        oob_score=False,
        warm_start=False,
        sampling_strategy="auto",
        replacement=False,
        n_jobs=None,
        random_state=None,
        verbose=0,
        sampler=None,
    ):

Import

from imblearn.ensemble import BalancedBaggingClassifier

I/O Contract

Inputs

Name	Type	Required	Description
X	{array-like, sparse matrix} of shape (n_samples, n_features)	Yes	Training features
y	array-like of shape (n_samples,)	Yes	Target labels
sampler	sampler object or None	No	Sampler for per-bag balancing (default: RandomUnderSampler)

Outputs

Name	Type	Description
fit() returns	self	Fitted ensemble
predict() returns	ndarray of shape (n_samples,)	Predicted labels

Usage Examples

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from imblearn.ensemble import BalancedBaggingClassifier

X, y = make_classification(n_classes=2, weights=[0.1, 0.9], n_samples=1000, random_state=0)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

bbc = BalancedBaggingClassifier(n_estimators=10, random_state=0)
bbc.fit(X_train, y_train)
print(f"Score: {bbc.score(X_test, y_test):.3f}")

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