Säännöllistämistekijöiden
Säännöllistämistekijät, often translated as regularization terms or coefficients, are a crucial concept in machine learning and statistical modeling. They are additive components incorporated into the cost function of a model during the training process. The primary purpose of säännöllistämistekijät is 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.
By adding a penalty term that discourages overly complex models, regularization helps to improve generalization. This
Two common types of regularization are L1 and L2 regularization. L1 regularization, also known as Lasso, adds