% Generated by roxygen2: do not edit by hand % Please edit documentation in R/R6_AgghooCV.R \name{AgghooCV} \alias{AgghooCV} \title{R6 class with agghoo functions fit() and predict().} \description{ Class encapsulating the methods to run to obtain the best predictor from the list of models (see 'Model' class). } \section{Methods}{ \subsection{Public methods}{ \itemize{ \item \href{#method-new}{\code{AgghooCV$new()}} \item \href{#method-fit}{\code{AgghooCV$fit()}} \item \href{#method-predict}{\code{AgghooCV$predict()}} \item \href{#method-getParams}{\code{AgghooCV$getParams()}} \item \href{#method-clone}{\code{AgghooCV$clone()}} } } \if{html}{\out{
}} \if{html}{\out{}} \if{latex}{\out{\hypertarget{method-new}{}}} \subsection{Method \code{new()}}{ Create a new AgghooCV object. \subsection{Usage}{ \if{html}{\out{
}}\preformatted{AgghooCV$new(data, target, task, gmodel, loss = NULL)}\if{html}{\out{
}} } \subsection{Arguments}{ \if{html}{\out{
}} \describe{ \item{\code{data}}{Matrix or data.frame} \item{\code{target}}{Vector of targets (generally numeric or factor)} \item{\code{task}}{"regression" or "classification"} \item{\code{gmodel}}{Generic model returning a predictive function} \item{\code{loss}}{Function assessing the error of a prediction} } \if{html}{\out{
}} } } \if{html}{\out{
}} \if{html}{\out{}} \if{latex}{\out{\hypertarget{method-fit}{}}} \subsection{Method \code{fit()}}{ Fit an agghoo model. \subsection{Usage}{ \if{html}{\out{
}}\preformatted{AgghooCV$fit(CV = list(type = "MC", V = 10, test_size = 0.2, shuffle = TRUE))}\if{html}{\out{
}} } \subsection{Arguments}{ \if{html}{\out{
}} \describe{ \item{\code{CV}}{List describing cross-validation to run. Slots: \cr - type: 'vfold' or 'MC' for Monte-Carlo (default: MC) \cr - V: number of runs (default: 10) \cr - test_size: percentage of data in the test dataset, for MC \cr (irrelevant for V-fold). Default: 0.2. - shuffle: wether or not to shuffle data before V-fold. Irrelevant for Monte-Carlo; default: TRUE} } \if{html}{\out{
}} } } \if{html}{\out{
}} \if{html}{\out{}} \if{latex}{\out{\hypertarget{method-predict}{}}} \subsection{Method \code{predict()}}{ Predict an agghoo model (after calling fit()) \subsection{Usage}{ \if{html}{\out{
}}\preformatted{AgghooCV$predict(X)}\if{html}{\out{
}} } \subsection{Arguments}{ \if{html}{\out{
}} \describe{ \item{\code{X}}{Matrix or data.frame to predict} } \if{html}{\out{
}} } } \if{html}{\out{
}} \if{html}{\out{}} \if{latex}{\out{\hypertarget{method-getParams}{}}} \subsection{Method \code{getParams()}}{ Return the list of V best parameters (after calling fit()) \subsection{Usage}{ \if{html}{\out{
}}\preformatted{AgghooCV$getParams()}\if{html}{\out{
}} } } \if{html}{\out{
}} \if{html}{\out{}} \if{latex}{\out{\hypertarget{method-clone}{}}} \subsection{Method \code{clone()}}{ The objects of this class are cloneable with this method. \subsection{Usage}{ \if{html}{\out{
}}\preformatted{AgghooCV$clone(deep = FALSE)}\if{html}{\out{
}} } \subsection{Arguments}{ \if{html}{\out{
}} \describe{ \item{\code{deep}}{Whether to make a deep clone.} } \if{html}{\out{
}} } } }