The Radius Neighbors Classifier is another type of supervised machine learning algorithm that can be used for classification tasks. It is similar to the K-Nearest Neighbors algorithm, but instead of using a fixed number of nearest neighbors, it considers all training data points within a fixed radius around the new data point.

The algorithm works by first defining a radius, which determines the size of the neighborhood around the new data point. Then, it searches the training data for all points within this radius. The class of the new data point is determined by a majority vote of the points within the radius.

The Radius Neighbors Classifier is a useful algorithm when the density of the training data is not uniform and the number of nearest neighbors is variable. However, like KNN, it can be computationally expensive for large datasets.

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