class_docstr: 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 :ref:`User Guide <nearest_centroid_classifier>`.
  
  Parameters
  ----------
  metric : string, or callable
      The metric to use when calculating distance between instances in a
      feature array. If metric is a string or callable, it must be one of
      the options allowed by metrics.pairwise.pairwise_distances for its
      metric parameter.
      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.
  
  shrink_threshold : float, optional (default = None)
      Threshold for shrinking centroids to remove features.
  
  Attributes
  ----------
  centroids_ : array-like, shape = [n_classes, n_features]
      Centroid of each class
  
  Examples
  --------
  >>> from sklearn.neighbors.nearest_centroid 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(metric='euclidean', shrink_threshold=None)
  >>> print(clf.predict([[-0.8, -1]]))
  [1]
  
  See also
  --------
  sklearn.neighbors.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.
code: def __init__(self, metric="euclidean", shrink_threshold=None):
      self.metric = metric
      self.shrink_threshold = shrink_threshold
  
file_path: catboost/contrib/python/scikit-learn/py2/sklearn/neighbors/nearest_centroid.py
imports: import warnings
  import numpy
  from scipy import sparse
  from ..base import BaseEstimator, ClassifierMixin
  from ..metrics.pairwise import pairwise_distances
  from ..preprocessing import LabelEncoder
  from ..utils.validation import check_array, check_X_y, check_is_fitted
  from ..utils.sparsefuncs import csc_median_axis_0
  from ..utils.multiclass import check_classification_targets
label_desc: Use image node_id 1 to create a new NearestCentroid object from inherited base classes: BaseEstimator, ClassifierMixin with example: obj = NearestCentroid(metric, shrink_threshold)