regularisointimenetelmän

Regularisointimenetelmä, or regularization method, 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 outliers, leading to poor performance on new, unseen data. Regularization introduces a penalty term into the model's objective function, which discourages complex models. This penalty term is typically based on the magnitude of the model's parameters. By adding this penalty, the model is forced to find a balance between fitting the training data and maintaining simplicity, thus improving its generalization ability.

There are several common types of regularization. L1 regularization, also known as Lasso, adds a penalty proportional to the absolute value of the coefficients. This can drive some coefficients to exactly zero, effectively performing feature selection. L2 regularization, or Ridge, adds a penalty proportional to the square of the coefficients. This shrinks coefficients towards zero but rarely makes them exactly zero. Elastic Net combines both L1 and L2 penalties. The choice of regularization method and the strength of the penalty (controlled by a regularization parameter, often denoted by lambda) are important hyperparameters that need to be tuned for optimal performance. Regularization is a crucial tool for building robust and reliable predictive models.