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Nearest Centroid Classification#

Sample usage of Nearest Centroid classification. It will plot the decision boundaries for each class.

3-Class classification (shrink_threshold=None)
3-Class classification (shrink_threshold=0.2)

None 0.8133333333333334
0.2 0.82

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
importmatplotlib.pyplotasplt
importnumpyasnp
frommatplotlib.colorsimport ListedColormap
fromsklearnimport datasets
fromsklearn.inspectionimport DecisionBoundaryDisplay
fromsklearn.neighborsimport NearestCentroid
# import some data to play with
iris = datasets.load_iris ()
# we only take the first two features. We could avoid this ugly
# slicing by using a two-dim dataset
X = iris.data[:, :2]
y = iris.target
# Create color maps
cmap_light = ListedColormap (["orange", "cyan", "cornflowerblue"])
cmap_bold = ListedColormap (["darkorange", "c", "darkblue"])
for shrinkage in [None, 0.2]:
 # we create an instance of Nearest Centroid Classifier and fit the data.
 clf = NearestCentroid (shrink_threshold=shrinkage)
 clf.fit(X, y)
 y_pred = clf.predict(X)
 print(shrinkage, np.mean (y == y_pred))
 _, ax = plt.subplots ()
 DecisionBoundaryDisplay.from_estimator (
 clf, X, cmap=cmap_light, ax=ax, response_method="predict"
 )
 # Plot also the training points
 plt.scatter (X[:, 0], X[:, 1], c=y, cmap=cmap_bold, edgecolor="k", s=20)
 plt.title ("3-Class classification (shrink_threshold=%r)" % shrinkage)
 plt.axis ("tight")
plt.show ()