Lassoregressioon
Lassoregressioon, also known as Lass regression, is a statistical method used for analyzing the relationship between a dependent variable and one or more independent variables. It is a form of linear regression that emphasizes the minimization of the sum of squared residuals to find the best-fitting linear model. The primary goal of lassoregressioon is to improve prediction accuracy and interpretability, especially when dealing with high-dimensional data where the number of variables exceeds the number of observations.
The technique incorporates regularization by adding a penalty term to the least squares criterion, which discourages
Lassoregressioon is widely used in fields such as genomics, finance, and machine learning, where the data often
Overall, lassoregressioon provides a robust framework for modeling complex datasets with a large number of predictors,