Description: Entropy regularization is a technique used in the field of machine learning to improve a model’s generalization ability. It involves adding an entropy term to the loss function during the training process. This term acts as a regulator that penalizes overly confident predictions, thereby promoting a more uniform probability distribution among classes. By doing so, it aims to prevent overfitting, a phenomenon where the model adapts too closely to the training data, losing its ability to generalize to unseen data. Entropy regularization is based on the principle that a model producing more balanced probability distributions is less likely to make incorrect predictions in unknown situations. This technique has become particularly relevant in the context of complex and deep models, where the number of parameters can lead to excessive fitting. In summary, entropy regularization not only enhances the model’s robustness but also contributes to better interpretation of outputs, as it encourages greater uncertainty in predictions when data is ambiguous or difficult to classify.
