What are some common variable selection techniques in machine learning?

What are some common variable selection techniques in machine learning?

Some common techniques include forward selection, backward elimination, and LASSO regression. Would you like to know more about any specific technique?

Can you explain LASSO regression?

LASSO regression adds a penalty equal to the absolute value of the magnitude of coefficients. This helps in feature selection by shrinking some coefficients to zero, effectively removing them from the model.

What is the difference between forward selection and backward elimination?

Forward selection starts with no variables and adds them one by one based on a criterion, while backward elimination starts with all variables and removes them one by one.

Which technique is generally preferred?

It depends on the dataset and the problem at hand. Forward selection is useful when you have a large number of variables, while backward elimination can be better when you start with a smaller set.