Introduction
Neighbors-based classification is a type of instance-based learning or non-generalizing learning: it does not attempt to construct a general internal model, but simply stores instances of the training data. Classification is computed from a simple majority vote of the nearest neighbors of each point: a query point is assigned the data class which has the most representatives within the nearest neighbors of the point.
Implementation
scikit-learn implements two different nearest neighbors classifiers: KNeighborsClassifier implements learning based on the k nearest neighbors of each query point, where k is an integer value specified by the user. RadiusNeighborsClassifier implements learning based on the number of neighbors within a fixed radius r of each training point, where r is a floating-point value specified by the user.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import neighbors, datasetsn_neighbors = 15 # import some data to play with
iris = datasets.load_iris()
X = iris.data[:, :2] # we only take the first two features.
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'])# we create an instance of Neighbours Classifier and fit the data. clf = neighbors.KNeighborsClassifier(n_neighbors)
clf.fit(X, y) # Plot the decision boundary. For that, we will assign 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)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.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.show()
Now let’s see how RadiusNeighborsClassifier works. After playing a bit with hyperparameters, we’ll achieve the following plot:
...
clf = neighbors.RadiusNeighborsClassifier(3.0, weights='distance') ...
Conclusion
Despite its simplicity, nearest neighbors has been successful in a large number of classification and regression problems, including handwritten digits or satellite image scenes. Being a non-parametric method, it is often successful in classification situations where the decision boundary is very irregular.
Originally published at https://deeprnd.blogspot.com on April 28, 2019.
