Regularisointitermin

Regularisointitermin, also known as regularization term, is a mathematical concept used in various fields such as machine learning, statistics, and optimization. It refers to an additional term added to a loss function to prevent overfitting and improve the generalization of a model. The primary goal of a regularization term is to constrain or regularize the complexity of the model, thereby reducing the risk of fitting noise in the training data.

One of the most commonly used regularization terms is the L2 norm, also known as Ridge regularization. This term is proportional to the square of the magnitude of the coefficients in the model. Another popular regularization term is the L1 norm, or Lasso regularization, which is proportional to the absolute value of the coefficients. Elastic Net regularization combines both L1 and L2 norms to leverage the strengths of both methods.

Regularization terms are typically controlled by a hyperparameter, often denoted as lambda (λ), which determines the strength of the regularization. A higher value of λ imposes a stronger penalty on the coefficients, leading to simpler models with potentially lower variance but higher bias. Conversely, a lower value of λ allows the model to fit the training data more closely, potentially increasing variance but reducing bias.

Regularization terms are widely used in various machine learning algorithms, including linear regression, logistic regression, and neural networks. They play a crucial role in improving the robustness and performance of models by balancing the trade-off between bias and variance.