👉 Neighbor math is a method used in machine learning, particularly in the context of neural networks and deep learning, where predictions or activations are compared to their neighbors rather than the entire dataset. This approach is especially useful when dealing with small datasets or when the data distribution is highly imbalanced. Instead of calculating the average or sum of activations across the entire batch, neighbor math computes a weighted average based on the activations of nearby data points (neighbors). These neighbors are typically selected using techniques like k-nearest neighbors, where each data point is considered a neighbor to others within a specified distance or similarity threshold. This method can provide more stable and interpretable results, as it focuses on local patterns and relationships within the data rather than global trends. It's particularly beneficial in scenarios where the model needs to generalize better from limited data, as it helps in capturing the nuances of the local structure.
