gpe_cv.glmnet

Default "penalizer function" generator <code>gpe</code> which uses <code>cv.glmnet</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

<dl><dt>...</dt>
<dd>arguments to <code>cv.glmnet</code>. <code>x</code>, <code>y</code>, <code>weights</code> and <code>family</code> will not be used.</dd></dl>

Arguments

Default penalized trainer for gpe — gpe_cv.glmnet

<dl>

<dt>...</dt>
<dd>arguments to <code>cv.glmnet</code>. <code>x</code>, <code>y</code>, <code>weights</code> and <code>family</code> will not be used.</dd>

</dl>

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gpe_cv.glmnet: Default penalized trainer for gpe

Description

Usage

Value

Arguments

See Also