k-Nearest Neighbors Insights

What is k-Nearest Neighbors?

K-Nearest Neighbors (k-NN) is a simple, supervised machine learning algorithm used for both classification and regression. It works by finding the 'k' nearest data points (neighbors) in the training dataset to a new, unlabeled data point, based on a distance metric (e.g., Euclidean distance). For classification, the new point is assigned the class most frequent among its 'k' neighbors. For regression, the predicted value is the average (or weighted average) of the values of its 'k' neighbors. k-NN is often used for its simplicity and ease of implementation, and can be applied in various fields such as pattern recognition, image recognition, and recommendation systems.

Find organizations using k-Nearest Neighbors on Sumble →

What other technologies are related to k-Nearest Neighbors?

k-Nearest Neighbors Competitor Technologies

Decision Forests

Decision Forests, including Random Forests, are alternative supervised learning algorithms that can perform similar classification and regression tasks as k-Nearest Neighbors.

mentioned alongside k-Nearest Neighbors in 19% (123) of relevant job posts

Naive Bayes

Naive Bayes is a classification algorithm that offers an alternative approach to k-Nearest Neighbors for classification problems.

mentioned alongside k-Nearest Neighbors in 14% (148) of relevant job posts

Linear & Logistic Regression

Linear and Logistic Regression provide alternative methods for regression and classification, respectively, compared to k-Nearest Neighbors.

mentioned alongside k-Nearest Neighbors in 17% (64) of relevant job posts

CHAID

CHAID is a decision tree algorithm that is a competitor to k-Nearest Neighbors for classification tasks.

mentioned alongside k-Nearest Neighbors in 18% (56) of relevant job posts

SVM

Support Vector Machines (SVM) is a powerful classification algorithm that serves as an alternative to k-Nearest Neighbors.

mentioned alongside k-Nearest Neighbors in 4% (148) of relevant job posts

Support Vector Machines

Support Vector Machines (SVM) is a powerful classification algorithm that serves as an alternative to k-Nearest Neighbors.

mentioned alongside k-Nearest Neighbors in 6% (79) of relevant job posts

Random Forests

Random Forests are ensemble learning methods using decision trees, and provide an alternative to k-Nearest Neighbors for classification and regression.

mentioned alongside k-Nearest Neighbors in 5% (103) of relevant job posts

CART

CART (Classification and Regression Trees) is a decision tree algorithm and a competitor to k-Nearest Neighbors for classification and regression tasks.

mentioned alongside k-Nearest Neighbors in 6% (56) of relevant job posts

k-Nearest Neighbors Complementary Technologies

Gibbs Sampling

Gibbs Sampling can be used for inference in k-Nearest Neighbors, especially for probabilistic versions or when dealing with missing data.

mentioned alongside k-Nearest Neighbors in 58% (57) of relevant job posts

Principal Component Analysis

Principal Component Analysis (PCA) can be used for dimensionality reduction before applying k-Nearest Neighbors, improving its performance and reducing computational cost.

mentioned alongside k-Nearest Neighbors in 15% (59) of relevant job posts

factor analysis

Factor Analysis can be used for dimensionality reduction before applying k-Nearest Neighbors, improving its performance and reducing computational cost.

mentioned alongside k-Nearest Neighbors in 12% (59) of relevant job posts