säännöllisestyksen

Säännöllisestyksen is a Finnish term that translates to "regularization" in English, a concept widely used in statistics and machine learning. It refers to techniques employed to prevent overfitting, a phenomenon where a model learns the training data too well, including its noise and specific characteristics, leading to poor performance on unseen data. Regularization methods aim to simplify the model by adding a penalty term to the objective function that discourages overly complex parameter values.

One common form of regularization is L1 regularization, also known as Lasso. This method adds a penalty proportional to the absolute value of the model's coefficients. L1 regularization can drive some coefficients to exactly zero, effectively performing feature selection by discarding irrelevant features.

Another widely used technique is L2 regularization, often called Ridge regression. L2 regularization penalizes the square of the magnitude of the coefficients. Unlike L1, it shrinks coefficients towards zero but rarely sets them to exactly zero, thus retaining all features while reducing their impact.

Elastic Net regularization combines both L1 and L2 penalties, offering a balance between feature selection and coefficient shrinkage. The choice of regularization technique and its strength, controlled by a hyperparameter, depends on the specific problem and dataset. Regularization is a crucial tool for building robust and generalizable models.