Säännöllistämisprosessia

Säännöllistämisprosessi, commonly referred to as regularization in English, is a technique used in statistical modeling and machine learning to prevent overfitting. Overfitting occurs when a model learns the training data too well, including its noise and outliers, leading to poor performance on unseen data. Regularization aims to improve the generalization ability of the model by adding a penalty term to the loss function.

The core idea behind regularization is to discourage overly complex models. By penalizing large coefficient values, regularization effectively simplifies the model, making it less sensitive to the specific nuances of the training set. This leads to a model that captures the underlying patterns rather than memorizing the training examples.

There are several types of regularization techniques, with L1 (Lasso) and L2 (Ridge) regularization being the most prominent. L1 regularization adds a penalty proportional to the absolute value of the coefficients, which can drive some coefficients to exactly zero, effectively performing feature selection. L2 regularization adds a penalty proportional to the square of the coefficients, shrinking them towards zero but rarely setting them to exactly zero. The choice between L1 and L2 regularization often depends on the specific problem and the desired outcome, such as sparsity in the model. Regularization is a crucial tool for building robust and reliable predictive models.