What Is Regularization?
In machine learning, regularization is a technique for reducing overfitting by adding a penalty term to the loss function. It is a form of regression that constrains the model to prevent it from learning the training data too well.
Regularization is a common practice in machine learning and statistical modeling. It is used to prevent overfitting, which occurs when a model performs well on the training data but does not generalize well to new data.
The process involves adding a penalty term to the loss function that penalizes the model for learning the training data too well. The penalty term is typically a function of the model parameters, such as the sum of the squared weights or the sum of the absolute values of the weights. The penalty term is added to the loss function to create a new loss function that is minimized during training.
Regularization can be used to compare different models or to tune the parameters of a model. It can also be used to select the best features for a model or to determine the optimal number of features.