KNN is a simple, easy-to-comprehend, and easy-to-implement classification algorithm. It can also be used for regression problems. KNN can be employed in lots of use cases, such as item recommendations and classification problems. Specifically, it can suggest movies on Netflix, articles on Medium, candidates on naukari.com, products on eBay, and videos on YouTube. In classification, it can be used to classify instances such as, for example, banking institutes that can classify the loan of risky candidates, or political scientists can classify potential voters.
KNN has three basic properties, which are non-parametric, lazy learner, and instance-based learning. Non-parametric means the algorithm is distribution-free and there is no need for parameters such as mean and standard deviation. Lazy learner means KNN does not train the model; that is, the model is trained in the testing phase. This makes for faster training but slower testing. It is also more time- and memory...
