Here you’ll find everything about k-nearest neighbor (k-NN), including tutorials on implementing the algorithm in Python.
The k-nearest neighbor algorithm is a method for classifying data points based on their similarity to other data points. It is a non-parametric, lazy learning algorithm that is used for both classification and regression. In k-NN, a data point is assigned to the class that is most common among its k nearest neighbors. The number of neighbors is a positive integer (k) that is specified by the user. The algorithm determines the neighbors by using a distance metric, such as Euclidean distance, that measures the similarity between data points. k-NN is known for its simplicity and versatility, but it can be computationally expensive. It also may not work well on high-dimensional data. Application areas include image recognition, speech recognition, and gene expression analysis.
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