Implementation:Scikit learn contrib Imbalanced learn RUSBoostClassifier

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 boosting with per-iteration random under-sampling provided by the imbalanced-learn library.

Description

The RUSBoostClassifier extends sklearn.ensemble.AdaBoostClassifier by applying RandomUnderSampler at each boosting iteration before training the weak learner. Supports custom base estimators and learning rate.

Usage

Import this class as a drop-in replacement for AdaBoostClassifier when dealing with imbalanced data.

Code Reference

Source Location

Repository: imbalanced-learn
File: imblearn/ensemble/_weight_boosting.py
Lines: L30-414

Signature

class RUSBoostClassifier(AdaBoostClassifier):
    def __init__(
        self,
        estimator=None,
        *,
        n_estimators=50,
        learning_rate=1.0,
        algorithm="deprecated",
        sampling_strategy="auto",
        replacement=False,
        random_state=None,
    ):

Import

from imblearn.ensemble import RUSBoostClassifier

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
n_estimators	int	No	Number of boosting stages (default: 50)
learning_rate	float	No	Shrinkage factor (default: 1.0)

Outputs

Name	Type	Description
fit() returns	self	Fitted boosted classifier
predict() returns	ndarray	Predicted labels

Usage Examples

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

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)

rusboost = RUSBoostClassifier(n_estimators=50, random_state=0)
rusboost.fit(X_train, y_train)
print(f"Score: {rusboost.score(X_test, y_test):.3f}")

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