👉 Margin engineering is a technique used in machine learning, particularly in deep learning and neural networks, to improve model performance by adjusting the margin or buffer space around decision boundaries. The idea is to create a larger "buffer" around the separating hyperplane that distinguishes between different classes in the data, making it harder for misclassifications to occur due to small variations in input data. By increasing this margin, the model becomes more robust and less sensitive to noise or outliers in the training data. This is achieved through various methods, such as adjusting regularization parameters, modifying loss functions, or employing techniques like margin-based optimization. The goal is to enhance the model's generalization ability, ensuring it performs well not just on the training data but also on unseen data.
