Regularisointimenetelmä
Regularisointimenetelmä, known in English as regularization, is a technique used in machine learning and statistics to prevent overfitting. Overfitting occurs when a model learns the training data too well, capturing noise and specific details that do not generalize to new, unseen data. Regularization introduces a penalty term to the model's cost function, which discourages overly complex models.
The core idea behind regularization is to add a constraint on the model's parameters. By penalizing large
Two common types of regularization are L1 regularization (Lasso) and L2 regularization (Ridge). L1 regularization adds