class sklearn.neighbors.NearestCentroid(metric='euclidean', *, shrink_threshold=None)[source]#

Nearest centroid classifier.

Each class is represented by its centroid, with test samples classified to the class with the nearest centroid.

Read more in the User Guide.

metric{“euclidean”, “manhattan”}, default=”euclidean”

Metric to use for distance computation.

If metric="euclidean", the centroid for the samples corresponding to each class is the arithmetic mean, which minimizes the sum of squared L1 distances. If metric="manhattan", the centroid is the feature-wise median, which minimizes the sum of L1 distances.

Changed in version 1.5: All metrics but "euclidean" and "manhattan" were deprecated and now raise an error.

Changed in version 0.19: metric='precomputed' was deprecated and now raises an error

shrink_thresholdfloat, default=None

Threshold for shrinking centroids to remove features.

centroids_array-like of shape (n_classes, n_features)

Centroid of each class.

classes_array of shape (n_classes,)

The unique classes labels.


Number of features seen during fit.

Added in version 0.24.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen during fit. Defined only when X has feature names that are all strings.

Added in version 1.0.

See also


Nearest neighbors classifier.


When used for text classification with tf-idf vectors, this classifier is also known as the Rocchio classifier.


Tibshirani, R., Hastie, T., Narasimhan, B., & Chu, G. (2002). Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proceedings of the National Academy of Sciences of the United States of America, 99(10), 6567-6572. The National Academy of Sciences.


>>> from sklearn.neighbors import NearestCentroid
>>> import numpy as np
>>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
>>> y = np.array([1, 1, 1, 2, 2, 2])
>>> clf = NearestCentroid()
>>>, y)
>>> print(clf.predict([[-0.8, -1]]))

For a more detailed example see: Nearest Centroid Classification

fit(X, y)[source]#

Fit the NearestCentroid model according to the given training data.

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

Training vector, where n_samples is the number of samples and n_features is the number of features. Note that centroid shrinking cannot be used with sparse matrices.

yarray-like of shape (n_samples,)

Target values.


Fitted estimator.


Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.


A MetadataRequest encapsulating routing information.


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.


Perform classification on an array of test vectors X.

The predicted class C for each sample in X is returned.

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

Test samples.

Cndarray of shape (n_samples,)

The predicted classes.

score(X, y, sample_weight=None)[source]#

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Xarray-like of shape (n_samples, n_features)

Test samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True labels for X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.


Mean accuracy of self.predict(X) w.r.t. y.


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.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') NearestCentroid[source]#

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.


This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in score.


The updated object.