säännöllöintiä

Säännöllöintiä is a Finnish term that translates to "regularization" in English, a concept primarily used in statistics and machine learning. It refers to a set of techniques designed to prevent overfitting in statistical models. Overfitting occurs when a model learns the training data too well, including its noise and random fluctuations, leading to poor performance on unseen data.

The core idea behind regularization is to introduce a penalty term into the objective function of a model. This penalty discourages overly complex models by adding a cost associated with the magnitude of the model's parameters (coefficients). By penalizing large coefficients, regularization effectively shrinks them towards zero, thus simplifying the model and reducing its variance.

Two common types of regularization are L1 regularization (Lasso) and L2 regularization (Ridge). L1 regularization adds a penalty proportional to the absolute value of the coefficients, which can drive some coefficients exactly to zero, effectively performing feature selection. L2 regularization adds a penalty proportional to the square of the coefficients, which shrinks coefficients but rarely makes them exactly zero. Both methods aim to improve the generalization ability of the model, making it more robust and reliable when applied to new, independent datasets. The choice between L1 and L2, or other regularization techniques, often depends on the specific problem and the desired outcome.