Säännöllistyminen

Säännöllistyminen is a Finnish term that translates to "regularization" in English, primarily used in the context of statistics and machine learning. It refers to a collection of techniques used to prevent overfitting in statistical models, particularly in regression analysis. 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 model's objective function. This penalty discourages overly complex models by penalizing large coefficient values. By shrinking or even setting some coefficients to zero, regularization effectively simplifies the model, making it more generalizable to new data. Common types of regularization include L1 regularization (Lasso) and L2 regularization (Ridge). L1 regularization can lead to sparse models by driving some coefficients to exactly zero, performing implicit feature selection. L2 regularization shrinks coefficients towards zero but rarely makes them exactly zero. The choice between different regularization techniques depends on the specific problem and the desired properties of the model.