class imblearn.over_sampling.SMOTENC(categorical_features, *, sampling_strategy='auto', random_state=None, k_neighbors=5, n_jobs=None)[source]#

Synthetic Minority Over-sampling Technique for Nominal and Continuous.

Unlike SMOTE, SMOTE-NC for dataset containing numerical and categorical features. However, it is not designed to work with only categorical features.

Read more in the User Guide.

New in version 0.4.

categorical_featuresndarray of shape (n_cat_features,) or (n_features,)

Specified which features are categorical. Can either be:

  • array of indices specifying the categorical features;

  • mask array of shape (n_features, ) and bool dtype for which True indicates the categorical features.

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.

k_neighborsint or object, default=5

The nearest neighbors used to define the neighborhood of samples to use to generate the synthetic samples. You can pass:

  • an int corresponding to the number of neighbors to use. A ~sklearn.neighbors.NearestNeighbors instance will be fitted in this case.

  • an instance of a compatible nearest neighbors algorithm that should implement both methods kneighbors and kneighbors_graph. For instance, it could correspond to a NearestNeighbors but could be extended to any compatible class.

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.

Deprecated since version 0.10: n_jobs has been deprecated in 0.10 and will be removed in 0.12. It was previously used to set n_jobs of nearest neighbors algorithm. From now on, you can pass an estimator where n_jobs is already set instead.

See also


Over-sample using SMOTE.


Over-sample using the SMOTE variant specifically for categorical features only.


Over-sample using SVM-SMOTE variant.


Over-sample using Borderline-SMOTE variant.


Over-sample using ADASYN.


Over-sample applying a clustering before to oversample using SMOTE.


See the original paper [1] for more details.

Supports multi-class resampling. A one-vs.-rest scheme is used as originally proposed in [1].

See Compare over-sampling samplers, and Sample generator used in SMOTE-like samplers. # noqa



N. V. Chawla, K. W. Bowyer, L. O.Hall, W. P. Kegelmeyer, “SMOTE: synthetic minority over-sampling technique,” Journal of artificial intelligence research, 321-357, 2002.


>>> from collections import Counter
>>> from numpy.random import RandomState
>>> from sklearn.datasets import make_classification
>>> from imblearn.over_sampling import SMOTENC
>>> 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 dataset shape {X.shape}')
Original dataset shape (1000, 20)
>>> print(f'Original dataset samples per class {Counter(y)}')
Original dataset samples per class Counter({1: 900, 0: 100})
>>> # simulate the 2 last columns to be categorical features
>>> X[:, -2:] = RandomState(10).randint(0, 4, size=(1000, 2))
>>> sm = SMOTENC(random_state=42, categorical_features=[18, 19])
>>> X_res, y_res = sm.fit_resample(X, y)
>>> print(f'Resampled dataset samples per class {Counter(y_res)}')
Resampled dataset samples per class Counter({0: 900, 1: 900})


fit(X, y)

Check inputs and statistics of the sampler.

fit_resample(X, y)

Resample the dataset.


Get parameters for this estimator.


Set the parameters of this estimator.

fit(X, y)[source]#

Check inputs and statistics of the sampler.

You should use fit_resample in all cases.

X{array-like, dataframe, sparse matrix} of shape (n_samples, n_features)

Data array.

yarray-like of shape (n_samples,)

Target array.


Return the instance itself.

fit_resample(X, y)[source]#

Resample the dataset.

X{array-like, dataframe, sparse matrix} of shape (n_samples, n_features)

Matrix containing the data which have to be sampled.

yarray-like of shape (n_samples,)

Corresponding label for each sample in X.

X_resampled{array-like, dataframe, sparse matrix} of shape (n_samples_new, n_features)

The array containing the resampled data.

y_resampledarray-like of shape (n_samples_new,)

The corresponding label of X_resampled.


Get parameters for this estimator.

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.


Parameter names mapped to their values.


Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.


Estimator parameters.

selfestimator instance

Estimator instance.

Examples using imblearn.over_sampling.SMOTENC#