gpe_cv.glmnet

arguments to <code><a rd-options="" href="/link/cv.glmnet?package=pre&version=0.3.0" data-mini-rdoc="pre::cv.glmnet">cv.glmnet</a></code>. <code>x</code>, <code>y</code>, <code>weights</code> and <code>family</code> will not be used.

Default "penalizer function" generator <code><a rd-options="" href="/link/gpe?package=pre&version=0.3.0" data-mini-rdoc="pre::gpe">gpe</a></code> which uses <code><a rd-options="" href="/link/cv.glmnet?package=pre&version=0.3.0" data-mini-rdoc="pre::cv.glmnet">cv.glmnet</a></code>.

Derives prediction rule ensembles (PREs). Largely follows the
procedure for deriving PREs as described in Friedman & Popescu (2008;
<DOI:10.1214/07-AOAS148>), with several adjustments and improvements. The
main function pre() derives prediction rule ensembles consisting of rules
and/or linear terms for binary, count and continuous responses. Function
gpe() derives generalized prediction ensembles, consisting of rules, hinge
and linear functions of the predictor variables.

Marjolein Fokkema

Prediction Rule Ensembles

Benjamin Christoffersen

gpe_cv.glmnet function

arguments to <code><a rd-options='' href='cv.glmnet'>cv.glmnet</a></code>. <code>x</code>, <code>y</code>, <code>weights</code> and <code>family</code> will not be used.

Default "penalizer function" generator <code><a rd-options='' href='gpe'>gpe</a></code> which uses <code><a rd-options='' href='cv.glmnet'>cv.glmnet</a></code>.

gpe_cv.glmnet: Default penalized trainer for gpe

Description

Usage

Arguments

Value

See Also