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# `sklearn.neighbors`.NearestCentroid

class sklearn.neighbors.NearestCentroid(metric='euclidean', *, shrink_threshold=None)

  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.

  Parameters:
    metric str or callable, default=”euclidean”

      Metric to use for distance computation. See the documentation of scipy.spatial.distance and the metrics listed in `distance_metrics` for valid metric values. Note that “wminkowski”, “seuclidean” and “mahalanobis” are not supported.

      The centroids for the samples corresponding to each class is the point from which the sum of the distances (according to the metric) of all samples that belong to that particular class are minimized. If the `"manhattan"` metric is provided, this centroid is the median and for all other metrics, the centroid is now set to be the mean.

      Deprecated since version 1.3: Support for metrics other than `euclidean` and `manhattan` and for callables was deprecated in version 1.3 and will be removed in version 1.5.

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

    shrink_threshold float, default=None

      Threshold for shrinking centroids to remove features.

  Attributes:
    centroids_array-like of shape (n_classes, n_features)

      Centroid of each class.

    classes_array of shape (n_classes,)

      The unique classes labels.

    n_features_in_int

      Number of features seen during fit.

      New 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.

      New in version 1.0.

  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

  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.

  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]

  Methods

  `fit`(X, y) | Fit the NearestCentroid model according to the given training data.
  `get_metadata_routing`() | Get metadata routing of this object.
  `get_params`([deep]) | Get parameters for this estimator.
  `predict`(X) | Perform classification on an array of test vectors `X`.
  `score`(X, y[, sample_weight]) | Return the mean accuracy on the given test data and labels.
  `set_params`(**params) | Set the parameters of this estimator.
  `set_score_request`(*[, sample_weight]) | Request metadata passed to the `score` method.

  fit(X, y)

    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.

      y array-like of shape (n_samples,)

        Target values.

    Returns:
      self object

        Fitted estimator.

  get_metadata_routing()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    Returns:
      routing MetadataRequest

        A `MetadataRequest` encapsulating routing information.

  get_params(deep=True)

    Get parameters for this estimator.

    Parameters:
      deep bool, default=True

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

    Returns:
      params dict

        Parameter names mapped to their values.

  predict(X)

    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)

        Test samples.

    Returns:
      C ndarray of shape (n_samples,)

        The predicted classes.

    Notes

    If the metric constructor parameter is `"precomputed"`, `X` is assumed to be the distance matrix between the data to be predicted and `self.centroids_`.

  score(X, y, sample_weight=None)

    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:
      X array-like of shape (n_samples, n_features)

        Test samples.

      y array-like of shape (n_samples,) or (n_samples, n_outputs)

        True labels for `X`.

      sample_weight array-like of shape (n_samples,), default=None

        Sample weights.

    Returns:
      score float

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

  set_params(**params)

    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:
      **params dict

        Estimator parameters.

    Returns:
      self estimator instance

        Estimator instance.

  set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → NearestCentroid

    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.

    New 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_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `sample_weight` parameter in `score`.

    Returns:
      self object

        The updated object.

## Examples using `sklearn.neighbors.NearestCentroid`

Nearest Centroid Classification

Nearest Centroid Classification

Classification of text documents using sparse features

Classification of text documents using sparse features