A simple explanation of the Lasso and Least Angle Regression

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. Whe