Implementation:Scikit learn contrib Imbalanced learn ADASYN

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 adaptive synthetic minority oversampling provided by the imbalanced-learn library.

Description

The ADASYN class implements the Adaptive Synthetic Sampling algorithm. It extends BaseOverSampler and generates different numbers of synthetic samples per minority instance based on the local density of majority class neighbors. Instances near the decision boundary receive more synthetic samples.

Usage

Import this class when the minority class has regions of varying difficulty and you want the oversampler to focus on harder-to-classify areas rather than uniformly oversampling.

Code Reference

Source Location

Repository: imbalanced-learn
File: imblearn/over_sampling/_adasyn.py
Lines: L23-215

Signature

class ADASYN(BaseOverSampler):
    def __init__(
        self,
        *,
        sampling_strategy="auto",
        random_state=None,
        n_neighbors=5,
    ):
        """
        Args:
            sampling_strategy: str, dict, or callable - Desired ratio of
                minority to majority samples. 'auto' equalizes all classes.
            random_state: int, RandomState, or None - Seed for reproducibility.
            n_neighbors: int or NearestNeighbors - Number of nearest neighbors
                used to compute density ratio and generate samples (default: 5).
        """

Import

from imblearn.over_sampling import ADASYN

I/O Contract

Inputs

Name	Type	Required	Description
X	{array-like, sparse matrix, dataframe} of shape (n_samples, n_features)	Yes	Feature matrix of training data
y	array-like of shape (n_samples,)	Yes	Target labels
sampling_strategy	str, dict, or callable	No	Resampling ratio (default: 'auto')
n_neighbors	int or NearestNeighbors	No	Neighbors for density estimation (default: 5)
random_state	int, RandomState, or None	No	Random seed

Outputs

Name	Type	Description
X_resampled	{ndarray, sparse matrix, dataframe} of shape (n_samples_new, n_features)	Feature matrix with adaptive synthetic samples added
y_resampled	ndarray of shape (n_samples_new,)	Target array with labels for synthetic samples

Usage Examples

Basic Usage

from collections import Counter
from sklearn.datasets import make_classification
from imblearn.over_sampling import ADASYN

# Create imbalanced dataset
X, y = make_classification(
    n_classes=2, class_sep=2, weights=[0.1, 0.9],
    n_informative=3, n_redundant=1, flip_y=0,
    n_features=20, n_clusters_per_class=1,
    n_samples=1000, random_state=10,
)
print(f"Original: {Counter(y)}")

# Apply ADASYN
adasyn = ADASYN(random_state=42)
X_res, y_res = adasyn.fit_resample(X, y)
print(f"Resampled: {Counter(y_res)}")

In a Pipeline

from imblearn.pipeline import make_pipeline
from imblearn.over_sampling import ADASYN
from sklearn.tree import DecisionTreeClassifier

pipeline = make_pipeline(ADASYN(random_state=42), DecisionTreeClassifier())
pipeline.fit(X_train, y_train)
y_pred = pipeline.predict(X_test)

Related Pages

Implements Principle

Principle:Scikit_learn_contrib_Imbalanced_learn_Adaptive_Synthetic_Sampling

Requires Environment

Environment:Scikit_learn_contrib_Imbalanced_learn_Python_Scikit_learn

Uses Heuristic

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Principle

Implementation

Heuristic

Environment