Penalty Term
In the context of machine learning, a penalty term is a regularization technique used to prevent overfitting of a model. It is added to the loss function of the model during training to discourage the model from fitting the training data too closely. The penalty term adds a cost to the loss function for large weights, which helps to keep the weights small and the model simpler. There are different types of penalty terms, such as L1 regularization (Lasso), L2 regularization (Ridge), and Elastic Net regularization, each with its own way of penalizing the weights. Penalty terms are particularly useful when dealing with high-dimensional data, where the number of features is much larger than the number of samples, as they help to select the most important features and avoid overfitting.
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