Säännöllistämön

Säännöllistämön is a Finnish term that translates to "regularization term" or "regularizer" in English, commonly used in the context of machine learning and statistical modeling. It refers to an additional component added to the loss function of a model during training. The primary purpose of a säännöllistämön 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.

By incorporating a säännöllistämön, the model is penalized for having excessively complex parameters or weights. This encourages the model to find simpler solutions that are more likely to generalize effectively. Different types of säännöllistämön exist, with L1 and L2 regularization being the most prevalent. L1 regularization, also known as Lasso, adds the sum of the absolute values of the weights to the loss function. This can lead to sparse models where some weights are driven to zero, effectively performing feature selection. L2 regularization, or Ridge, adds the sum of the squared values of the weights to the loss function. This tends to shrink the weights towards zero without necessarily making them exactly zero, resulting in a more robust model. The choice of säännöllistämön and its strength, often controlled by a hyperparameter, is a crucial aspect of model tuning to achieve optimal performance.