# A simple explanation of the Lasso and Least Angle Regression

Give a set of input measurements x1, x2 ...xp and an outcome measurement y, the lasso fits a linear model

yhat=b0 + b1*x1+ b2*x2 + ... bp*xp

The criterion it uses is:

Minimize sum( (y-yhat)^2 ) subject to sum[absolute value(bj)] <= s

The first sum is taken over observations (cases) in the dataset. The bound "s" is a tuning parameter. When "s" is large enough, the constraint has no effect and the solution is just the usual multiple linear least squares regression of y on x1, x2, ...xp.

However when for smaller values of s (s>=0) the solutions are shrunken versions of the least squares estimates. Often, some of the coefficients bj are zero. Choosing "s" is like choosing the number of predictors to use in a regression model, and cross-validation is a good tool for estimating the best value for "s".

## Computation of the Lasso solutions

The computation of the lasso solutions is a quadratic programming problem, and can be tackled by standard numerical analysis algorithms. But the least angle regression procedure is a better approach. This algorithm exploits the special structure of the lasso problem, and provides an efficient way to compute the solutions simulataneously for all values of "s".

Least angle regression is like a more "democratic" version of forward stepwise regression. Recall how forward stepwise regression works:

#### Forward stepwise regression algorithm:

• Find the predictor xj most correlated with y, and add it into the model. Take residuals r= y-yhat.
• Continue, at each stage adding to the model the predictor most correlated with r.
• Until: all predictors are in the model
The least angle regression procedure follows the same general scheme, but doesn't add a predictor fully into the model. The coefficient of that predictor is increased only until that predictor is no longer the one most correlated with the residual r. Then some other competing predictor is invited to "join the club".