For "importance" it's probably best to stick with absolute values of
coefficients, instead of value of the penalty parameter for which the
coefficients changed to non-zero.
Friedman skipped a lot of details on his rule ensemble in that talk, due to
time constraint. In his implementation he was us
Zubin,
my understanding about lasso is that it is a restricted version of
regression, where minimization of sse subject to sum(abs(beta)) < upper
limit such that for unimportant feature, its beta will be restricted by
ZERO. the whole game of lasso is to find the proper upper limit. I think in
lass
Attended JSM last week and Friedman mentioned the use of LASSO for
variable selection (he uses it for rules ensembles). I am an
econometrician and not familiar with, i started running the examples in
R this week and you get to the plots section of the LARS package.
Plots of beta/max(beta) v
