3.4.8.12. Nearest-neighbor prediction on irisΒΆ

Plot the decision boundary of nearest neighbor decision on iris, first with a single nearest neighbor, and then using 3 nearest neighbors.

import numpy as np
import matplotlib.pyplot as plt
from sklearn import neighbors, datasets
from matplotlib.colors import ListedColormap
# Create color maps for 3-class classification problem, as with iris
cmap_light = ListedColormap(["#FFAAAA", "#AAFFAA", "#AAAAFF"])
cmap_bold = ListedColormap(["#FF0000", "#00FF00", "#0000FF"])
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
knn = neighbors.KNeighborsClassifier(n_neighbors=1)
knn.fit(X, y)
x_min, x_max = X[:, 0].min() - 0.1, X[:, 0].max() + 0.1
y_min, y_max = X[:, 1].min() - 0.1, X[:, 1].max() + 0.1
xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100), np.linspace(y_min, y_max, 100))
Z = knn.predict(np.c_[xx.ravel(), yy.ravel()])

Put the result into a color plot

Z = Z.reshape(xx.shape)
plt.figure()
plt.pcolormesh(xx, yy, Z, cmap=cmap_light)
# Plot also the training points
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)
plt.xlabel("sepal length (cm)")
plt.ylabel("sepal width (cm)")
plt.axis("tight")
plot iris knn
(4.180808080808081, 8.019191919191918, 1.8868686868686868, 4.513131313131313)

And now, redo the analysis with 3 neighbors

knn = neighbors.KNeighborsClassifier(n_neighbors=3)
knn.fit(X, y)
Z = knn.predict(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.figure()
plt.pcolormesh(xx, yy, Z, cmap=cmap_light)
# Plot also the training points
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)
plt.xlabel("sepal length (cm)")
plt.ylabel("sepal width (cm)")
plt.axis("tight")
plt.show()
plot iris knn

Total running time of the script: (0 minutes 0.756 seconds)

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