Data analysts and data scientists use different regression methods for different kinds of analytics problems. From the simplest ones to the most complex ones. One of the most talked-about methods is the Lasso. Lasso was often described as one of the most useful linear regression tools and we are about to find out why.<\/p>\n\n\n\n
LASSO is actually an abbreviation for \u201cLeast absolute shrinkage and selection operator\u201d, which basically summarizes how Lasso regression works. Lasso does regression analysis using a shrinkage parameter \u201cwhere data are shrunk to a certain central point\u201d [1<\/a>] and performs variable selection by forcing the coefficients of \u201cnot-so-significant\u201d variables to become zero through a penalty.<\/p>\n\n\n\n Now to understand more about this powerful tool, we will apply\nthis example to a real-world problem.<\/p>\n\n\n\n