Co-Authored By:

**LASSO**regression is a type of regression analysis in which both variable selection and regulization occurs simultaneously. This method uses a

**penalty**which affects they value of coefficients of regression. As

**penalty**increases more coefficients are becomes zero and vice Versa.

Click to see full answer.

Also to know is, how does lasso work?

**LASSO**, is actually an acronym for Least Absolute Selection and Shrinkage Operator. The **LASSO** imposes a constraint on the sum of the absolute values of the model parameters, where the sum has a specified constant as an upper bound. This constraint causes regression coefficients for some variables to shrink towards zero.

Subsequently, question is, what is the difference between Ridge and lasso regression? The only **difference** from **Ridge regression** is that the regularization term is in absolute value. **Lasso** method overcomes the disadvantage of **Ridge regression** by not only punishing high values of the coefficients β but actually setting them to zero if they are not relevant.

what is lasso used for?

In statistics and machine learning, **lasso** (least absolute shrinkage and selection operator; also **Lasso** or **LASSO**) is a regression analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the statistical model it produces.

What is Lasso and Ridge?

**Ridge** and **Lasso** regression are powerful techniques generally used for creating parsimonious models in presence of a 'large' number of features. Here 'large' can typically mean either of two things: Large enough to enhance the tendency of a model to overfit (as low as 10 variables might cause overfitting)