How to Answer: What is the difference between K-Nearest Neighbors and k-means clustering?
Advice and answer examples written specifically for a Machine Learning job interview.
17. What is the difference between K-Nearest Neighbors and k-means clustering?
How to Answer
When interviewing for a role as a machine learning engineer, you will be asked a wide range of technical questions related to the field. The best way to prepare for this is to review the concepts, terms, processes, and procedures you use in your job. You should also carefully read the job description and research the organization to understand the type of work it does. Finally, practicing questions like these will help you become familiar with the type of question you will be asked and the best way to respond to them.
Written by William Swansen on January 18th, 2021
Answer Example
"The main difference between K-Nearest Neighbors and K-means clustering is that K-Nearest Neighbors is a supervised classification algorithm, while K-means is unsupervised. Supervised classification algorithms label the data and assign it to groups, while unsupervised ones do not. K-Nearest Neighbors uses the labeled data to classify an unlabeled point. K-means clustering will learn how to cluster unlabeled points by computing the means of the distance between different points."
Written by William Swansen on January 18th, 2021