K Nearest Neighbor - Learn Data Science with Travis

K-Nearest-Neighbor

K-Nearest Neighbor (KNN) is a fundamental supervised machine learning algorithm widely used for classification and regression tasks. It operates on the principle of proximity, classifying a data point based on the majority class of its ‘k’ closest neighbors in the feature space. The value of ‘k’ is predetermined and influences the algorithm’s sensitivity to noise and outliers. KNN is non-parametric, meaning it makes no assumptions about the underlying data distribution, making it versatile across various applications, from image recognition to recommendation systems. Its simplicity and effectiveness make it a popular choice for both beginners and experienced data scientists.

What is the K-Nearest Neighbor?

Towards Data Science

K-Nearest Neighbor (KNN) is an easy to understand, but essential and broadly applicable supervised machine learning technique. To understand the intuition behind KNN, examine the scatterplot below…

Get to know your k-Nearest Neighbor

Analytics Vidhya

K-nearest neighbors algorithm (k-NN) is a supervised, instance-based, non-parametric algorithm which makes use of the k closest examples in the feature space. Supervised means that it needs to be fed…...

How Neighborly is K-Nearest Neighbors to GIS Pros?

Towards AI

Created by the author with DALL E-3 At one point in your life I am sure you have interacted with a nice neighbor, you know, the one who would greet you on your way to work or school, ask how your day ...

Everything You Ever Wanted to Know About K-Nearest Neighbors

Towards Data Science

K-Nearest Neighbors is one of the simplest and easiest to understand machine learning algorithms. It can be used for both classification and regression tasks but is more common in classification, so…

K-Nearest Neighbors

Towards Data Science

A perfect opening line I must say for presenting the K-Nearest Neighbors. Yes, that's how simple the concept behind KNN is. It just classifies a data point based on its few nearest neighbors. How…

K- Nearest Neighbors(KNN):A Simple Introduction

Analytics Vidhya

K Nearest Neighbors is a classification algorithm that operates on a very simple principle. Building the model consists only of storing the training dataset. To make a prediction for a new data…

K-nearest Neighbor: The maths behind it, how it works and an example

Analytics Vidhya

K-nearest Neighbor (KNN) is a supervised classification algorithm that is based on predicting data by finding the similarities to the underlying data. KNN is most widely used for classification…

K-Nearest Neighbor(k-NN)

Analytics Vidhya

The k-nearest neighbors (K-NN) algorithm is a simple, easy to implement supervised machine learning algorithm. The “K” in k-NN refers to the number of nearest neighbors it will take into…

K-Nearest Neighbors From a Theoretical Perspective

Analytics Vidhya

The k nearest neighbors (k-NN) is one of the oldest supervised machine learning algorithms. It was created by Cover and Hart in 1967. We notice a data set with the points scattered on the graph and…

K-Nearest Neighbors: Theory and Practice

Towards Data Science

K-Nearest Neighbors, also known as KNN, is probably one of the most intuitive algorithms there is, and it works for both classification and regression tasks. Since it is so easy to understand, it is…

K-Nearest Neighbours explained

Towards Data Science

K-Nearest Neighbours (KNN here onwards) is an intuitive and easy to understand machine learning algorithm. This post provides a short introduction to KNN. We will first learn how the algorithm works…

Build K-Nearest Neighbors KNN from Scratch

Python in Plain English

In this world — you are the average of the K datapoints you hang around Photo by Solen Feyissa on Unsplash Introduction A wise person once said “you’re the average of the five people you hang around ...