Regularisaatiokerroin

Regularisaatiokerroin, also known as a regularization coefficient or penalty parameter, is a parameter used in regularization techniques to prevent overfitting in machine learning models. Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern, leading to poor generalization on new, unseen data. Regularization techniques add a penalty to the loss function to constrain or regularize the model parameters, thereby reducing the complexity of the model.

The regularization coefficient controls the strength of this penalty. A higher regularization coefficient imposes a stronger penalty, leading to simpler models with smaller coefficients. Conversely, a lower regularization coefficient allows the model to fit the training data more closely, potentially leading to overfitting. The optimal value of the regularization coefficient is typically determined through cross-validation, where the model's performance is evaluated on a validation set for different values of the coefficient.

Regularization techniques that use a regularization coefficient include Lasso (Least Absolute Shrinkage and Selection Operator), Ridge regression, and Elastic Net. Lasso uses the L1 norm of the coefficients as the penalty, which can lead to sparse models with some coefficients exactly equal to zero. Ridge regression uses the L2 norm, which shrinks the coefficients but does not set them to zero. Elastic Net combines both L1 and L2 penalties, providing a balance between sparsity and coefficient shrinkage.

In summary, the regularization coefficient is a crucial parameter in regularization techniques, influencing the trade-off between model complexity and fit to the training data. Proper tuning of this coefficient is essential for building robust and generalizable machine learning models.