Säännöllistämistekijöitä

Säännöllistämistekijöitä, known in English as regularization parameters, are crucial concepts in machine learning and statistical modeling. They are used to prevent overfitting, a phenomenon where a model learns the training data too well, including its noise, and consequently performs poorly on unseen data. Regularization parameters control the strength of the regularization applied to the model.

The core idea behind regularization is to add a penalty term to the model's loss function. This penalty discourages complex models by adding a cost for large parameter values. The regularization parameter, often denoted by lambda (λ) or alpha (α), determines how much this penalty influences the model's optimization process. A larger regularization parameter leads to a stronger penalty and thus a simpler model, while a smaller parameter allows for more complexity.

Common forms of regularization include L1 (Lasso) and L2 (Ridge) regularization. L1 regularization adds a penalty proportional to the absolute value of the weights, which can drive some weights to exactly zero, effectively performing feature selection. L2 regularization adds a penalty proportional to the square of the weights, which tends to shrink all weights towards zero but rarely makes them exactly zero. The choice of the regularization parameter is critical and is often determined through techniques like cross-validation, where different values are tested to find the one that yields the best performance on a validation set.