class imblearn.over_sampling.ADASYN(*, sampling_strategy='auto', random_state=None, n_neighbors=5, n_jobs=None)[source]

Oversample using Adaptive Synthetic (ADASYN) algorithm.

This method is similar to SMOTE but it generates different number of samples depending on an estimate of the local distribution of the class to be oversampled.

Read more in the User Guide.

sampling_strategyfloat, str, dict or callable, default=’auto’

Sampling information to resample the data set.

  • When float, it corresponds to the desired ratio of the number of samples in the minority class over the number of samples in the majority class after resampling. Therefore, the ratio is expressed as \alpha_{os} = N_{rm} / N_{M} where N_{rm} is the number of samples in the minority class after resampling and N_{M} is the number of samples in the majority class.


    float is only available for binary classification. An error is raised for multi-class classification.

  • When str, specify the class targeted by the resampling. The number of samples in the different classes will be equalized. Possible choices are:

    'minority': resample only the minority class;

    'not minority': resample all classes but the minority class;

    'not majority': resample all classes but the majority class;

    'all': resample all classes;

    'auto': equivalent to 'not majority'.

  • When dict, the keys correspond to the targeted classes. The values correspond to the desired number of samples for each targeted class.

  • When callable, function taking y and returns a dict. The keys correspond to the targeted classes. The values correspond to the desired number of samples for each class.

random_stateint, RandomState instance, default=None

Control the randomization of the algorithm.

  • If int, random_state is the seed used by the random number generator;

  • If RandomState instance, random_state is the random number generator;

  • If None, the random number generator is the RandomState instance used by np.random.

n_neighborsint int or object, optional (default=5)

If int, number of nearest neighbours to used to construct synthetic samples. If object, an estimator that inherits from sklearn.neighbors.base.KNeighborsMixin that will be used to find the k_neighbors.

n_jobsint, default=None

Number of CPU cores used during the cross-validation loop. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

See also


Over-sample using SMOTE.


The implementation is based on [1].

Supports multi-class resampling. A one-vs.-rest scheme is used.



He, Haibo, Yang Bai, Edwardo A. Garcia, and Shutao Li. “ADASYN: Adaptive synthetic sampling approach for imbalanced learning,” In IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp. 1322-1328, 2008.


>>> from collections import Counter
>>> from sklearn.datasets import make_classification
>>> from imblearn.over_sampling import ADASYN 
>>> 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('Original dataset shape %s' % Counter(y))
Original dataset shape Counter({1: 900, 0: 100})
>>> ada = ADASYN(random_state=42)
>>> X_res, y_res = ada.fit_resample(X, y)
>>> print('Resampled dataset shape %s' % Counter(y_res))
Resampled dataset shape Counter({0: 904, 1: 900})
__init__(self, *, sampling_strategy='auto', random_state=None, n_neighbors=5, n_jobs=None)[source]

Initialize self. See help(type(self)) for accurate signature.