Machine Learning with Python Cookbook, 2nd Edition

Chapter 15. K-Nearest Neighbors

15.0 Introduction

The k-nearest neighbors (KNN) classifier is one of the simplest yet most commonly used classifiers in supervised machine learning. KNN is often considered a lazy learner; it doesn’t technically train a model to make predictions. Instead an observation is predicted to be the same class as that of the largest proportion of the k nearest observations.

For example, if an observation with an unknown class is surrounded by an observation of class 1, then the observation is classified as class 1. In this chapter we will explore how to use scikit-learn to create and use a KNN classifier.

15.1 Finding an Observation’s Nearest Neighbors

Problem

You need to find an observation’s k nearest observations (neighbors).

Solution

Use scikit-learn’s NearestNeighbors:

# Load libraries
from sklearn import datasets
from sklearn.neighbors import NearestNeighbors
from sklearn.preprocessing import StandardScaler

# Load data
iris = datasets.load_iris()
features = iris.data

# Create standardizer
standardizer = StandardScaler()

# Standardize features
features_standardized = standardizer.fit_transform(features)

# Two nearest neighbors
nearest_neighbors = NearestNeighbors(n_neighbors=2).fit(features_standardized)

# Create an observation
new_observation = [ 1,  1,  1,  1]

# Find distances and indices of the observation's nearest neighbors
distances, indices = nearest_neighbors.kneighbors([new_observation])

# View the nearest neighbors
features_standardized ...