Regularisointitermi

Regularisointitermi, also known as regularization term, is a mathematical concept used in various fields, including machine learning and statistics, to prevent overfitting and improve the generalization of models. It is a penalty added to the loss function that the model aims to minimize. The regularization term typically involves the model parameters, such as weights in a neural network or coefficients in a regression model.

One of the most common types of regularization is L2 regularization, also known as Ridge regression. It adds a penalty proportional to the square of the magnitude of the coefficients. This encourages the model to keep the coefficients small, effectively reducing the complexity of the model. Another type is L1 regularization, or Lasso regression, which adds a penalty proportional to the absolute value of the coefficients. This can lead to sparse models, where some coefficients are exactly zero, effectively performing feature selection.

Regularization terms can also be combined, leading to Elastic Net regularization, which incorporates both L1 and L2 penalties. This approach can provide a balance between the benefits of both types of regularization.

The choice of regularization term and its strength (controlled by a hyperparameter) is crucial and often determined through cross-validation. Regularization helps in building models that not only fit the training data well but also perform well on unseen data, making it an essential tool in the toolkit of data scientists and machine learning practitioners.