Implementation:Scikit learn contrib Imbalanced learn make imbalance

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

Overview

Concrete tool for creating imbalanced datasets from balanced data provided by the imbalanced-learn library.

Description

The make_imbalance function takes an existing dataset and reduces class counts according to a sampling_strategy dict or callable. Internally it uses RandomUnderSampler to perform the reduction.

Usage

Import this function to create controlled imbalanced datasets for testing, benchmarking, or demonstration purposes.

Code Reference

Source Location

Repository: imbalanced-learn
File: imblearn/datasets/_imbalance.py
Lines: L27-118

Signature

def make_imbalance(
    X, y, *, sampling_strategy=None, random_state=None, verbose=False, **kwargs
):
    """
    Args:
        X: {array-like, dataframe} of shape (n_samples, n_features) - Data.
        y: array-like of shape (n_samples,) - Labels.
        sampling_strategy: dict or callable - Target class counts.
        random_state: int, RandomState, or None - Seed.
        verbose: bool - Print distribution info (default: False).
    Returns:
        X_resampled, y_resampled - Imbalanced dataset.
    """

Import

from imblearn.datasets import make_imbalance

I/O Contract

Inputs

Name	Type	Required	Description
X	{array-like, dataframe} of shape (n_samples, n_features)	Yes	Data matrix
y	array-like of shape (n_samples,)	Yes	Target labels
sampling_strategy	dict or callable	Yes	Target counts per class
random_state	int, RandomState, or None	No	Random seed

Outputs

Name	Type	Description
X_resampled	{ndarray, dataframe} of shape (n_samples_new, n_features)	Imbalanced data
y_resampled	ndarray of shape (n_samples_new,)	Imbalanced labels

Usage Examples

from collections import Counter
from sklearn.datasets import load_iris
from imblearn.datasets import make_imbalance

data = load_iris()
X, y = data.data, data.target
print(f"Before: {Counter(y)}")

X_res, y_res = make_imbalance(
    X, y,
    sampling_strategy={0: 10, 1: 20, 2: 30},
    random_state=42,
)
print(f"After: {Counter(y_res)}")

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