Säännöllistämistekijät

Säännöllistämistekijät, known in English as regularization terms or penalties, are mathematical functions added to the objective function of a machine learning model during training. Their primary purpose 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 adding a regularization term, the model is penalized for having overly complex parameters, which encourages simpler and more generalized solutions.

The most common types of regularization are L1 and L2 regularization. L1 regularization, also known as Lasso, adds the absolute value of the model's weights to the objective function. This can drive some weights to exactly zero, effectively performing feature selection by removing irrelevant features. L2 regularization, also known as Ridge, adds the square of the model's weights to the objective function. This shrinks the weights towards zero but rarely makes them exactly zero, thus retaining all features but reducing their impact.

The strength of the regularization is controlled by a hyperparameter, often denoted by lambda (λ). A higher lambda value imposes a stronger penalty on model complexity, leading to more regularization. The optimal value of lambda is typically found through cross-validation. Säännöllistämistekijät are a crucial tool for building robust and reliable machine learning models that perform well in real-world applications.