regulariseringsterm

A "regulariseringsterm" (regularization term) is a component added to an optimization problem or a cost function in machine learning and statistical modeling to prevent overfitting and improve the model's generalization ability. Regularization introduces a penalty for more complex models, encouraging simpler solutions that are less likely to fit noise in the training data.

In the context of machine learning, regulariseringsterm is often incorporated into algorithms such as linear regression, logistic regression, and neural networks. By adding a regularization term, the model's parameters are constrained, which reduces variance and mitigates overfitting, especially when dealing with high-dimensional data.

Common types of regulariseringsterm include L1 regularization (Lasso), which adds the absolute value of the magnitude of coefficients to the loss function, promoting sparsity in the model parameters. L2 regularization ( Ridge) incorporates the squared magnitude of coefficients, encouraging smaller, more evenly distributed weights.

Mathematically, a typical objective function with a regulariseringsterm can be expressed as:

Loss function + λ * Regulariseringsterm

where λ (lambda) is a hyperparameter controlling the strength of regularization. The choice of regulariseringsterm and the regularization parameter depends on the specific problem and desired properties of the model.

Overall, a regulariseringsterm is a crucial tool in machine learning for balancing model complexity and accuracy, leading to models that perform better on unseen data.