K-Nearest Neighbor
K-Nearest Neighbor (KNN) is a non-parametric machine learning algorithm used for classification and regression. In KNN, the output is a class membership or a real value based on the k closest training examples in the feature space. KNN is a type of instance-based learning, or lazy learning, where the function is only approximated locally and all computation is deferred until classification. The KNN algorithm assumes that similar things exist in close proximity. In other words, similar things are near to each other. KNN can be used for both regression and classification tasks, and it is easy to understand and implement. However, the main drawback of KNN is that it can be computationally expensive, especially when dealing with large datasets.
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