Notes:

k-Nearest Neighbors (k-NN) is a supervised machine learning algorithm used for classification and regression. In k-NN, a sample is classified based on the majority class of its k nearest neighbors. k is a positive integer and is a hyperparameter that determines the number of nearest neighbors to consider when making a prediction.

For example, if k = 3, the algorithm will consider the three nearest neighbors of a sample and predict the class that is most common among those neighbors. The distance between samples is usually measured using a distance metric, such as Euclidean distance or Manhattan distance.

k-NN is a simple and intuitive algorithm that is easy to implement and can be used for a wide range of applications. However, it can be computationally expensive, as the algorithm requires storing and processing all the training data in order to make predictions. In addition, k-NN can be sensitive to the choice of k, and choosing an appropriate value for k can be challenging.

See also:

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