KNN, or K-Nearest Neighbors, is a supervised machine learning algorithm primarily used for classification tasks. It operates by identifying the ‘k’ closest data points in the feature space to a given input and classifying the input based on the majority class among those neighbors. This algorithm is particularly effective for both binary and multi-class classification problems, making it versatile for various applications, including economic forecasting and other domains where classification is required ²³.

One of the key advantages of KNN is its simplicity and ease of implementation. It does not require any specific assumptions about the underlying data distribution, which allows it to work with a wide range of datasets ³. However, KNN can be computationally intensive, especially as the size of the dataset increases, since it involves calculating distances between points. Techniques like K-D Trees and Ball Trees can be employed to optimize the distance calculations and improve efficiency ³.

In summary, KNN is a powerful and straightforward algorithm for classification tasks, particularly when dealing with multi-class problems, but it may face challenges with larger datasets due to its computational demands ¹⁵.