Building a K-Nearest Neighbors classifier
A K-Nearest Neighbors classifier is a classification model that uses the nearest neighbors algorithm to classify a given data point. The algorithm finds the
K closest data points in the training dataset to identify the category of the input data point. It will then assign a class to this data point based on a majority vote. From the list of those
K data points, we look at the corresponding classes and pick the one with the highest number of votes. Let's see how to build a classifier using this model. The value of
K depends on the problem at hand.
Create a new Python file and import the following packages:
import numpy as np import matplotlib.pyplot as plt import matplotlib.cm as cm from sklearn import neighbors, ...