K-nearest neighbor Algorithm in Machine Learning
K-nearest neighbor classifier is one of the introductory supervised classifiers, which every data science learner should be aware of Fix & Hodges proposed K-nearest neighbor classifier algorithm in the year 1951 for performing pattern classification task.
For simplicity, this classifier is known as Knn Classifier. To be supervised k-nearest neighbor classifier is mostly represented as Knn, even in many research papers. Knn addresses the pattern recognition problems and also the best choices for addressing some of the classification-related tasks.
The simple version of the K-nearest neighbor classifier algorithm is to predict the target label by finding the nearest neighbor class. The closest class will be identified using the distance measures like Euclidean distance.
The k-nearest neighbor algorithm is a pattern recognition model that can be used for classification as well as regression. Often abbreviated as K-NN, the k in k-nearest neighbor is a positive integer, which is typically small. In either classification or regression, the input will consist of the k-closest training examples within a space.