k-NN Insights | Sumble

What is k-NN?

k-Nearest Neighbors (k-NN) is a simple, non-parametric algorithm used for both classification and regression. In k-NN classification, an object is classified by a majority vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). In k-NN regression, the output is the average (or weighted average) of the values of its k nearest neighbors. k-NN is widely used in pattern recognition, data mining, and intrusion detection.

Find organizations using k-NN on Sumble →

What other technologies are related to k-NN?

k-NN Competitor Technologies

Decision Forests

Decision Forests are an ensemble learning method for classification and regression, and therefore a competitor to k-NN.

mentioned alongside k-NN in 68% (441) of relevant job posts

Naive Bayes

Naive Bayes is a classification algorithm, and therefore a competitor to k-NN.

mentioned alongside k-NN in 51% (551) of relevant job posts

SVM

Support Vector Machines are a classification algorithm, and therefore a competitor to k-NN.

mentioned alongside k-NN in 18% (751) of relevant job posts

GPTs

GPTs are typically used for text generation and understanding tasks, and can be competitors in classification tasks.

mentioned alongside k-NN in 12% (60) of relevant job posts

decision trees

Decision trees are a classification algorithm, and therefore a competitor to k-NN.

mentioned alongside k-NN in 3% (179) of relevant job posts

Neural Network

Neural Networks are a classification and regression algorithm, and therefore a competitor to k-NN.

mentioned alongside k-NN in 7% (89) of relevant job posts

K-means

K-means is a clustering algorithm, it is a competitor to k-NN because k-NN can be adapted for unsupervised clustering-like tasks, even though it is not its primarily use.

mentioned alongside k-NN in 6% (61) of relevant job posts

Random Forest

Random Forest is an ensemble learning method for classification and regression, and therefore a competitor to k-NN.

mentioned alongside k-NN in 3% (128) of relevant job posts