Implementation:Scikit learn contrib Imbalanced learn SMOTEENN

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

Overview

Concrete tool for combined SMOTE oversampling and Edited Nearest Neighbours cleaning provided by the imbalanced-learn library.

Description

The SMOTEENN class combines SMOTE oversampling with ENN under-sampling in a two-step process. First it applies SMOTE to balance the dataset, then uses Edited Nearest Neighbours to remove any sample whose class label is misclassified by its neighbors, cleaning noisy or ambiguous regions. Users can provide custom SMOTE and ENN instances.

Usage

Import this class when SMOTE alone introduces noise near the decision boundary and a cleaning step is desired. SMOTEENN provides more aggressive cleaning than SMOTETomek.

Code Reference

Source Location

Repository: imbalanced-learn
File: imblearn/combine/_smote_enn.py
Lines: L25-160

Signature

class SMOTEENN(BaseSampler):
    def __init__(
        self,
        *,
        sampling_strategy="auto",
        random_state=None,
        smote=None,
        enn=None,
        n_jobs=None,
    ):
        """
        Args:
            sampling_strategy: str, dict, or callable - Resampling ratio.
            random_state: int, RandomState, or None - Seed.
            smote: SMOTE or None - SMOTE oversampler (default: SMOTE()).
            enn: EditedNearestNeighbours or None - ENN cleaner (default: ENN()).
            n_jobs: int or None - Parallel jobs.
        """

Import

from imblearn.combine import SMOTEENN

I/O Contract

Inputs

Name	Type	Required	Description
X	{array-like, sparse matrix} of shape (n_samples, n_features)	Yes	Feature matrix
y	array-like of shape (n_samples,)	Yes	Target labels
smote	SMOTE or None	No	Custom SMOTE instance (default: SMOTE())
enn	EditedNearestNeighbours or None	No	Custom ENN instance (default: ENN())

Outputs

Name	Type	Description
X_resampled	ndarray of shape (n_samples_new, n_features)	Oversampled and cleaned feature matrix
y_resampled	ndarray of shape (n_samples_new,)	Cleaned target array

Usage Examples

from collections import Counter
from sklearn.datasets import make_classification
from imblearn.combine import SMOTEENN

X, y = make_classification(
    n_classes=2, weights=[0.1, 0.9], n_samples=1000, random_state=10
)
smote_enn = SMOTEENN(random_state=42)
X_res, y_res = smote_enn.fit_resample(X, y)
print(f"Resampled: {Counter(y_res)}")

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