Class encapsulating the methods to run to obtain the best predictor
from the list of models (see 'Model' class).
}
-\section{Public fields}{
-\if{html}{\out{<div class="r6-fields">}}
-\describe{
-\item{\code{params}}{List of parameters of the V selected models}
-}
-\if{html}{\out{</div>}}
-}
\section{Methods}{
\subsection{Public methods}{
\itemize{
\subsection{Method \code{new()}}{
Create a new AgghooCV object.
\subsection{Usage}{
-\if{html}{\out{<div class="r">}}\preformatted{AgghooCV$new(data, target, task, gmodel, loss = NULL)}\if{html}{\out{</div>}}
+\if{html}{\out{<div class="r">}}\preformatted{AgghooCV$new(data, target, task, gmodel, loss)}\if{html}{\out{</div>}}
}
\subsection{Arguments}{
\subsection{Method \code{fit()}}{
Fit an agghoo model.
\subsection{Usage}{
-\if{html}{\out{<div class="r">}}\preformatted{AgghooCV$fit(CV = list(type = "MC", V = 10, test_size = 0.2, shuffle = TRUE))}\if{html}{\out{</div>}}
+\if{html}{\out{<div class="r">}}\preformatted{AgghooCV$fit(CV = NULL)}\if{html}{\out{</div>}}
}
\subsection{Arguments}{
\if{html}{\out{<div class="arguments">}}
\describe{
-\item{\code{CV}}{List describing cross-validation to run. Slots:
-- type: 'vfold' or 'MC' for Monte-Carlo (default: MC)
-- V: number of runs (default: 10)
-- test_size: percentage of data in the test dataset, for MC
- (irrelevant for V-fold). Default: 0.2.
-- shuffle: wether or not to shuffle data before V-fold.
- Irrelevant for Monte-Carlo; default: TRUE}
+\item{\code{CV}}{List describing cross-validation to run. Slots: \cr
+ - type: 'vfold' or 'MC' for Monte-Carlo (default: MC) \cr
+ - V: number of runs (default: 10) \cr
+ - test_size: percentage of data in the test dataset, for MC
+ (irrelevant for V-fold). Default: 0.2. \cr
+ - shuffle: wether or not to shuffle data before V-fold.
+ Irrelevant for Monte-Carlo; default: TRUE \cr
+Default (if NULL): type="MC", V=10, test_size=0.2}
}
\if{html}{\out{</div>}}
}