Radius Neighbors Classifier

The Radius Neighbors Classifier is a supervised machine learning algorithm utilized for classification tasks, akin to the K-Nearest Neighbors (KNN) algorithm. Its distinct feature is its reliance on a fixed radius to encompass training data points around a new data point, rather than a set number of nearest neighbors.

This classifier operates by first establishing a radius, which sets the scope of the neighborhood for the new data point. It then identifies all training points within this radius. The classification of the new point is deduced from a majority vote of the classes within the radius.

A key advantage of the Radius Neighbors Classifier is its effectiveness in scenarios where training data exhibits non-uniform density and requires a variable number of neighbors. However, similar to KNN, it faces challenges of computational intensity in handling large datasets.

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