Description: The weighted K-nearest neighbors is an approach that enhances the K-nearest neighbors (KNN) algorithm by assigning different weights to neighbors based on their proximity to the query point. Instead of treating all neighbors equally, this method gives more importance to those that are closer, which can lead to more accurate classification or regression. This approach is particularly useful in datasets where point density varies, allowing the model to better adapt to the local structure of the data. Weighting can be implemented in various ways, such as using inverse distance, where closer neighbors receive a higher weight, or applying kernel functions that smooth the influence of distant neighbors. This technique not only improves model accuracy but can also help mitigate the impact of noise in the data, as more distant points, which could be outliers, have less influence on the final prediction. In summary, weighting in KNN is a key strategy that seeks to maximize the effectiveness of the algorithm by considering the relevance of neighbors based on their closeness.
