Neighbourhood

Neighborhood-based algorithms are a class of machine learning algorithms that are based on the concept of finding the closest neighbors of a given data sample and using the information from these neighbors to make predictions. The most well-known example of this type of algorithm is the K-Nearest Neighbors (KNN) algorithm that I mentioned in my previous answer.

Other algorithms that belong to the neighborhood-based category include:

• Radial basis function networks (RBFNs): A type of neural network that uses radial basis functions as activation functions to interpolate the values of the input data.

• Local linear regression: A method for regression problems that fits a linear model to the locally weighted average of the training data around each test point.

• Locality-sensitive hashing (LSH): A technique for approximate nearest neighbor search that reduces the dimensionality of the data and hashes the data points into buckets such that similar data points are likely to be hashed into the same bucket.

• Neighborhood component analysis (NCA): A method for dimensionality reduction and visualization that preserves the local neighborhood structure of the data.

These algorithms share the common idea of finding the nearest neighbors of a test data sample and using the information from these neighbors to make predictions. However, the specific techniques used to find the neighbors and make predictions can vary greatly between algorithms.