Description: Model regularization is a fundamental technique in machine learning used to prevent overfitting, a phenomenon where a model fits too closely to the training data, losing its ability to generalize to new data. This approach involves adding a complexity penalty to the model, which restricts its flexibility and forces it to find a balance between accuracy on training data and model simplicity. There are various regularization techniques, such as L1 (Lasso) and L2 (Ridge), which modify the model’s cost function to include terms that penalize the coefficients of features. Regularization not only improves the model’s generalization ability but can also help identify relevant features by reducing the impact of those that are less significant. In an inference context, where computational resources are limited and response speed is crucial, regularization becomes an essential tool for optimizing models that must operate efficiently on devices with constrained capabilities. By applying these techniques, models can maintain robust performance without compromising speed or efficiency, which is vital in applications such as computer vision and natural language processing.
