Lassoregularisointi

Lassoregularisointi, also known as Lasso regularization, is a technique used in statistical modeling and machine learning to prevent overfitting. It is a type of regularized regression that adds a penalty term to the standard least squares cost function. This penalty is proportional to the absolute value of the magnitude of the coefficients of the linear model. The formula for Lasso regularization is:

Minimize (Sum of squared residuals + lambda * Sum of absolute values of coefficients)

Here, lambda is a tuning parameter that controls the strength of the penalty. A larger lambda value results in a stronger penalty and more coefficients being shrunk towards zero. A key characteristic of Lasso regularization is its ability to perform feature selection by driving some of the coefficients to exactly zero. This means that the corresponding features are effectively removed from the model, simplifying it and potentially improving its interpretability and generalization performance. Lasso is particularly useful when dealing with datasets that have a large number of features, some of which may be irrelevant or redundant. The choice of lambda is typically determined through cross-validation.