What is K-Nearest Neighbors?
Data regression and data classification are two common jobs you’ll perform as a data scientist in the future. The K-nearest neighbors algorithm will help you complete these tasks by predicting the correct class of test data. This article will answer the question, “What is K-nearest neighbors?” and explain the benefits of enrolling in one of The Data Incubator’s data science programs to advance your career.
K-Nearest Neighbors Meaning
K-nearest neighbors (KNN) is a supervised learning algorithm used in machine learning. Data scientists use it for data regression and data classification because it calculates the distance between test data — datasets that train algorithms — and the different training points in that data. It helps you predict the correct class of test data.
This definition from The Startup might help you understand the question, “What is K-nearest neighbors?” a little better:
“Suppose there are two categories, i.e., Category A and Category B, and we have a new data point x1, so this data point will lie in which of these categories. To solve this type of problem, we need a KNN algorithm. With the help of KNN, we can easily identify the category or class of a particular dataset.”
The “K” in K-nearest neighbors is the number of neighbors to consider when predicting the class of a training point in test data. “Neighbors” are the data points in a dataset closest to a specific query point. The KNN algorithm calculates the distance between query points and other points in the data and considers their values or labels.
The algorithm that would later become KNN originated from an unpublished report for the United States Air Force in 1951. Today, companies use KNN for various classification and regression tasks, such as classifying data for product recommendations on websites.
As you can see, KNN is a difficult concept to explain, but you’ll learn more about this algorithm in a data science program. You might use a Python machine learning library like Scikit-learn during your training to perform the KNN algorithm for you and check the nearest neighbors for different data points.
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How Does KNN Work?
The KNN algorithm predicts the correct class of test data by:
- Selecting the K-number of neighbors in an existing data set
- Calculating the distance of the K-number of those neighbors
- Counting the data points in each category in the data set
- Giving each category new data points
It’s important to note that there are different methods to calculate the distance of a K number in a data set. The most popular one is to use Euclidean distance, which determines the distance between two points in Euclidean space — a representation of physical space in geometry. Euclidean distance is the default option in Python’s SKlearn KNN classifier library.
Pros of KNN
Some of the benefits of using KNN include:
- Because KNN is a nonparametric algorithm — algorithms that don’t make specific assumptions about mapping functions — it’s valuable for nonlinear data.
- KNN provides a high level of accuracy when predicting test data classes, meaning you don’t need to compare results with those from other supervised learning models.
- KNN has several real-world data classification functions that you might use in your career as a data scientist. For example, YouTube uses KNN to provide video recommendations on users’ feeds.
Cons of KNN
Here are a couple of drawbacks of using KNN:
- When dealing with large data sets, the KNN algorithm takes a long time to make predictions.
- KNN requires training data for it to work properly. Companies need expensive computer systems and storage infrastructure to keep all this data.
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