Säännöllistämistä

Säännöllistämistä, often translated as regularization in English, is a technique used in statistics and machine learning to prevent overfitting. Overfitting occurs when a model learns the training data too well, including its noise and random fluctuations, leading to poor performance on new, unseen data. Regularization methods add a penalty term to the model's objective function, which discourages overly complex models.

The most common types of regularization are L1 (Lasso) and L2 (Ridge) regularization. L1 regularization adds a penalty proportional to the absolute value of the model's coefficients. This type of regularization can drive some coefficients exactly to zero, effectively performing feature selection by eliminating irrelevant features from the model. L2 regularization, on the other hand, adds a penalty proportional to the square of the coefficients. This method shrinks the coefficients towards zero but rarely makes them exactly zero, leading to a model with smaller, more distributed weights.

The strength of the regularization is controlled by a hyperparameter, often denoted by lambda (λ) or alpha (α). A larger value of this hyperparameter indicates stronger regularization. Choosing the appropriate regularization strength is crucial and is typically done through cross-validation, where different values are tested to find the one that yields the best performance on unseen data. Regularization is a fundamental tool for building robust and generalizable machine learning models.