Nearest Neighbors Classification¶

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

Python source code: plot_classification.py

print __doc__

import numpy as np
import pylab as pl
from matplotlib.colors import ListedColormap
from sklearn import neighbors, datasets

n_neighbors = 15

# import some data to play with
iris = datasets.load_iris()
X = iris.data[:, :2] # we only take the first two features. We could
                     # avoid this ugly slicing by using a two-dim dataset
y = iris.target

h = .02 # step size in the mesh

# Create color maps
cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])

for weights in ['uniform', 'distance']:
    # we create an instance of Neighbours Classifier and fit the data.
    clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)
    clf.fit(X, y)

    # Plot the decision boundary. For that, we will asign a color to each
    # point in the mesh [x_min, m_max]x[y_min, y_max].
    x_min, x_max = X[:,0].min()-1, X[:,0].max() + 1
    y_min, y_max = X[:,1].min()-1, X[:,1].max() + 1
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                         np.arange(y_min, y_max, h))
    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])

    # Put the result into a color plot
    Z = Z.reshape(xx.shape)
    pl.figure()
    pl.pcolormesh(xx, yy, Z, cmap=cmap_light)

    # Plot also the training points
    pl.scatter(X[:,0], X[:,1], c=y, cmap=cmap_bold)
    pl.title("3-Class classification (k = %i, weights = '%s')"
             % (n_neighbors, weights))
    pl.axis('tight')

pl.show()

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Nearest Neighbors Classification¶