Säännöllöinti

Säännöllöinti is a Finnish term that translates to "regularization" in English, particularly in the context of statistical modeling and machine learning. It refers to a set of techniques used to prevent overfitting, a common problem where a model learns the training data too well, including its noise and outliers, leading to poor performance on new, unseen data.

The core idea behind regularization is to add a penalty term to the model's loss function. This penalty discourages overly complex models by penalizing large coefficients or weights. By introducing this penalty, the model is encouraged to find simpler solutions that generalize better.

Common regularization techniques include L1 regularization (Lasso) and L2 regularization (Ridge). L1 regularization adds a penalty proportional to the absolute value of the coefficients, which can drive some coefficients exactly to zero, effectively performing feature selection. L2 regularization adds a penalty proportional to the square of the coefficients, which shrinks coefficients towards zero but rarely makes them exactly zero. Elastic Net regularization combines both L1 and L2 penalties.

The choice of regularization technique and the strength of the penalty (often controlled by a hyperparameter, lambda) are crucial for effective regularization. These parameters are typically tuned using cross-validation to find the optimal balance between fitting the training data and achieving good generalization performance. Säännöllöinti is an essential tool for building robust and reliable predictive models.