Explore the K-Nearest Neighbors (kNN) algorithm in this 26-minute lecture from NPTEL-NOC IITM. Learn about the fundamentals of kNN, including its applications, underlying assumptions, and step-by-step implementation. Discover when to use kNN and gain insights into crucial aspects such as parameter selection, feature scaling, and the importance of feature selection. Through illustrations and practical examples, understand how kNN works during the testing phase and grasp key considerations for effective implementation.
K-Nearest Neighbors (KNN) Algorithm - Introduction and Applications
Overview
Syllabus
Introduction
Why KNN and when does one use it?
k Nearest Neighbors
Assumptions
Algorithm
Illustration of KNN (Testing)
Things to consider
Parameter selection
Feature selection and scaling
Taught by
NPTEL-NOC IITM
