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 accuracy on provided 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]#

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config). Please check the 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.

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

NearestCentroid#

Gallery examples#

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