The K-nearest neighbors algorithm predicts from the labels or responses of the training observations closest to the query under a chosen distance.
The L-nearest-neighbour classifier estimates each class probability by its empirical frequency among the nearest training covariates and predicts a class of greatest estimated probability.
One-nearest-neighbour classification assigns a query the label of its single nearest training observation. Under local regularity, its limiting conditional error is .
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The K-nearest neighbors (KNN) algorithm is a simple and widely-used machine learning algorithm primarily used for classification and regression tasks. It is a type of instance-based learning, meaning it makes predictions based on the instances (data points) that are stored in the training set. ### Key Concepts: 1. **Instance-based learning**: KNN stores all of the training instances and makes decisions based on the instances it finds most similar to new data.
