säännöllistämismäärä

Säännöllistämismäärä is a Finnish term that translates to "regularization term" or "regularizer" in English. In the context of machine learning and statistical modeling, a regularization term is an addition to the objective function that penalizes complex models. Its primary purpose is to prevent overfitting, a phenomenon where a model learns the training data too well, including its noise and outliers, leading to poor performance on unseen data.

The concept of regularization is based on the principle of Occam's Razor, which suggests that simpler explanations are generally better than more complex ones. By adding a penalty for model complexity, regularization encourages the model to find a simpler, more generalized solution. Common types of regularization include L1 regularization (Lasso) and L2 regularization (Ridge). L1 regularization adds the absolute value of the weights to the objective function, which can lead to sparse models where some weights are exactly zero, effectively performing feature selection. L2 regularization adds the squared value of the weights, which tends to shrink weights towards zero but rarely makes them exactly zero. The choice of regularization technique and its strength, often controlled by a hyperparameter, can significantly impact the model's performance and generalization ability.