wNN

wNN, short for weighted nearest neighbors, is a machine learning algorithm used for classification and regression tasks. It is an extension of the k-nearest neighbors (k-NN) algorithm, which assigns a class or value to a data point based on the majority class or average value of its k nearest neighbors.

In wNN, each neighbor's influence is weighted based on its distance to the query point. The weights are typically inversely proportional to the distance, meaning closer neighbors have a greater influence on the prediction. This weighting scheme can improve the algorithm's performance, especially when the data has varying densities or when the neighbors are not equally relevant.

The choice of the weight function is crucial in wNN. Common weight functions include the inverse of the distance, the Gaussian function, and the exponential function. The performance of wNN can be sensitive to the choice of the weight function and the parameters involved, such as the number of neighbors (k) and the scaling factor for the distance.

wNN is relatively simple to implement and understand, making it a popular choice for many applications. However, it can be computationally expensive, especially for large datasets, as it requires calculating the distance between the query point and all other points in the dataset. Despite this, wNN remains a valuable tool in the machine learning toolkit, offering a balance between simplicity and performance.