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KNN (k-nearest neighbors) classification example

The K-Nearest-Neighbors algorithm is used below as a classification tool. The data set (Iris) has been used for this example.

The decision boundaries, are shown with all the points in the training-set.


Python source code:

print __doc__

# Code source: Gael Varoqueux
# Modified for Documentation merge by Jaques Grobler
# License: BSD

import numpy as np
import pylab as pl
from sklearn import neighbors, datasets

# import some data to play with
iris = datasets.load_iris()
X =[:, :2] # we only take the first two features. 
Y =

h = .02 # step size in the mesh


# we create an instance of Neighbours Classifier and fit the data., 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() - .5, X[:,0].max() + .5
y_min, y_max = X[:,1].min() - .5, X[:,1].max() + .5
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
Z = knn.predict(np.c_[xx.ravel(), yy.ravel()])

# Put the result into a color plot
Z = Z.reshape(xx.shape)
pl.figure(1, figsize=(4, 3))
pl.pcolormesh(xx, yy, Z)

# Plot also the training points
pl.scatter(X[:,0], X[:,1],c=Y )
pl.xlabel('Sepal length')
pl.ylabel('Sepal width')

pl.xlim(xx.min(), xx.max())
pl.ylim(yy.min(), yy.max())