Unveiling Dynamic Weighted KNN: A Deep Exploration of Adaptive and Weighted k-Nearest Neighbours

Introduction

In the landscape of machine learning, the k-Nearest Neighbours (k-NN) algorithm has long been celebrated for its simplicity and intuitive approach to classification and regression tasks. However, as datasets grow in complexity, the limitations of traditional k-NN become more apparent, particularly in its static selection of neighbors and uniform weighting.

Enter DynamicWeightedKNN — an enhanced version of k-NN that dynamically adjusts the number of neighbours (k) based on local data density and applies weighted influence to these neighbours depending on their proximity. In this article, we’ll delve into the theoretical underpinnings of DynamicWeightedKNN, break down its Python implementation, analyse its performance compared to other models, and discuss its practical applications.

The Limitations of Traditional k-NN

Before diving into the enhancements introduced by DynamicWeightedKNN, it’s essential to understand the inherent limitations of traditional k-NN:

1. Fixed Number of Neighbours (k): In traditional k-NN, the number of neighbours k is predetermined and remains fixed for all query points. This rigidity can lead to suboptimal…