👉 Neighbor engineering is a technique used in machine learning, particularly in the context of deep neural networks, to improve model performance by modifying or augmenting the input data around specific neurons. This is achieved by creating synthetic training examples, known as "neighborly" or "neighborhood" examples, which are generated by interpolating or transforming the existing data points. These modified examples are designed to be similar to the original data but with slight variations, helping the model learn more robust and generalized features. By training the network on these enhanced datasets, neighbor engineering can reduce overfitting, improve generalization to unseen data, and enhance the model's ability to handle noisy or incomplete inputs. This approach is especially useful when dealing with limited training data, as it effectively expands the effective size and diversity of the training set.
