gpe_cv.glmnet

arguments to <code><a rd-options="" href="/link/cv.glmnet?package=pre&version=1.0.2" 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=1.0.2" data-mini-rdoc="pre::gpe">gpe</a></code> which uses <code><a rd-options="" href="/link/cv.glmnet?package=pre&version=1.0.2" 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 adjustments and improvements. The
main function pre() derives prediction rule ensembles consisting of
rules and/or linear terms for continuous, binary, count, multinomial,
and multivariate 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>.

Last chance! 50% off unlimited learning

gpe_cv.glmnet: Default penalized trainer for gpe

Description

Usage

Arguments

Value

See Also