NearestCentroid#

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

Nearest centroid classifier.

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

See also

KNeighborsClassifier: Nearest neighbors classifier.

Notes

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

References

[1] 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.

[2] Hastie, T., Tibshirani, R., Friedman, J. (2009). The Elements of Statistical Learning Data Mining, Inference, and Prediction. 2nd Edition. New York, Springer.

Examples

>>> 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()
>>> clf.fit(X, y)
NearestCentroid()
>>> print(clf.predict([[-0.8, -1]]))
[1]

decision_function(X)[source]#

Apply decision function to an array of samples.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): Array of samples (test vectors).

Returns:

y_scoresndarray of shape (n_samples,) or (n_samples, n_classes): Decision function values related to each class, per sample. In the two-class case, the shape is (n_samples,), giving the log likelihood ratio of the positive class.

fit(X, y)[source]#

Fit the NearestCentroid model according to the given training data.

Parameters:

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.

Returns:

selfobject: Fitted estimator.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:

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

Returns:

paramsdict: Parameter names mapped to their values.

predict(X)[source]#

Perform classification on an array of test vectors X.

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

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): Input data.

Returns:

y_predndarray of shape (n_samples,): The predicted classes.

predict_log_proba(X)[source]#

Estimate log class probabilities.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): Input data.

Returns:

y_log_probandarray of shape (n_samples, n_classes): Estimated log probabilities.

predict_proba(X)[source]#

Estimate class probabilities.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): Input data.

Returns:

y_probandarray of shape (n_samples, n_classes): Probability estimate of the sample for each class in the model, where classes are ordered as they are in self.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.

Parameters:

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.

Returns:

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

set_params(**params)[source]#

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.

Parameters:

**paramsdict: Estimator parameters.

Returns:

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.

Note

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.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in score.

Returns:

selfobject: The updated object.

Gallery examples#

Nearest Centroid Classification

Classification of text documents using sparse features