.loss

observed values:
numeric vector of length \(n\)

predicted values:
numeric vector of length \(n\)

character <code>"gaussian"</code>, <code>"binomial"</code>, <code>"poisson"</code>,
<code>"mgaussian"</code>, or <code>"multinomial"</code> (to implement: <code>"cox"</code>)

family

character <code>"deviance"</code>, <code>"mse"</code>, <code>"mae"</code>, <code>"class"</code>,
or <code>"auc"</code>

type.measure

fold identifiers:
integer vector of length \(n\),
or <code>NULL</code>

foldid

logical (for <code>"cox"</code> only)

grouped

Calculate loss from predicted and observed values

Implements stacked elastic net regression (Rauschenberger 2020, <doi:10.1093/bioinformatics/btaa535>). The elastic net generalises ridge and lasso regularisation (Zou 2005, <doi:10.1111/j.1467-9868.2005.00503.x>). Instead of fixing or tuning the mixing parameter alpha, we combine multiple alpha by stacked generalisation (Wolpert 1992 <doi:10.1016/S0893-6080(05)80023-1>).

.loss: Loss

Description

Usage

Arguments

Examples