concept

K Nearest Neighbors

K Nearest Neighbors (KNN) is a simple, non-parametric, instance-based machine learning algorithm used for classification and regression tasks. It operates by finding the 'k' closest data points (neighbors) in the training set to a new input and making predictions based on the majority class (for classification) or average value (for regression) of these neighbors. It relies on distance metrics like Euclidean or Manhattan distance to measure similarity between data points.

Also known as: KNN, k-NN, k-Nearest Neighbors, K-Nearest Neighbors, Nearest Neighbor Algorithm
🧊Why learn K Nearest Neighbors?

Developers should learn KNN when working on small to medium-sized datasets where interpretability and simplicity are priorities, such as in recommendation systems, image recognition, or medical diagnosis. It's particularly useful as a baseline model due to its ease of implementation and no training phase, but it can be computationally expensive for large datasets and sensitive to irrelevant features.

Compare K Nearest Neighbors

K Nearest Neighbors vs Support Vector Machines→K Nearest Neighbors vs Decision Trees→K Nearest Neighbors vs Logistic Regression→

Learning Resources

📄
Scikit-learn Documentation: K-Nearest Neighbors
docs
→
🎬
K-Nearest Neighbors (KNN) - Machine Learning Basics with Python
video
→
📝
K-Nearest Neighbors Algorithm Tutorial
tutorial
→
📚
Introduction to Statistical Learning: K-Nearest Neighbors
book
→

Related Tools

Machine LearningClassification AlgorithmsRegression AlgorithmsDistance MetricsScikit Learn

Alternatives to K Nearest Neighbors

Support Vector MachinesDecision TreesLogistic Regression