Description: Overparameterization refers to a situation where a machine learning model has more parameters than can be justified by the available data’s quantity and quality. This can lead to the model fitting too closely to the training data, capturing noise rather than meaningful patterns. While it was traditionally seen as a problem, overparameterization has proven to be a common feature in deep learning models, where complex architectures like deep neural networks can have millions of parameters. In this context, overparameterization can allow the model to generalize better to new data, provided that appropriate regularization and validation techniques are used. The key is to find a balance between model complexity and data quantity, so that overfitting is avoided and the model’s generalization capability is maximized. This phenomenon has led to a shift in the perception of overparameterization, considering it not just a risk but also a potentially effective strategy in the design of various machine learning models.
