Update package to send on CRAN

[agghoo.git] / man / AgghooCV.Rd

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-% 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$clone()}}
-}
-}
-\if{html}{\out{<hr>}}
-\if{html}{\out{<a id="method-new"></a>}}
-\if{latex}{\out{\hypertarget{method-new}{}}}
-\subsection{Method \code{new()}}{
-Create a new AgghooCV object.
-\subsection{Usage}{
-\if{html}{\out{<div class="r">}}\preformatted{AgghooCV$new(data, target, task, gmodel, loss)}\if{html}{\out{</div>}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{<div class="arguments">}}
-\describe{
-\item{\code{data}}{Matrix or data.frame}
-
-\item{\code{target}}{Vector of targets (generally numeric or factor)}
-
-\item{\code{task}}{"regression" or "classification".
-Default: classification if target not numeric.}
-
-\item{\code{gmodel}}{Generic model returning a predictive function
-Default: tree if mixed data, knn/ppr otherwise.}
-
-\item{\code{loss}}{Function assessing the error of a prediction
-Default: error rate or mean(abs(error)).}
-}
-\if{html}{\out{</div>}}
-}
-}
-\if{html}{\out{<hr>}}
-\if{html}{\out{<a id="method-fit"></a>}}
-\if{latex}{\out{\hypertarget{method-fit}{}}}
-\subsection{Method \code{fit()}}{
-Fit an agghoo model.
-\subsection{Usage}{
-\if{html}{\out{<div class="r">}}\preformatted{AgghooCV$fit(CV = NULL)}\if{html}{\out{</div>}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{<div class="arguments">}}
-\describe{
-\item{\code{CV}}{List describing cross-validation to run. Slots: \cr
-  - type: 'vfold' or 'MC' for Monte-Carlo (default: MC) \cr
-  - V: number of runs (default: 10) \cr
-  - test_size: percentage of data in the test dataset, for MC
-    (irrelevant for V-fold). Default: 0.2. \cr
-  - shuffle: wether or not to shuffle data before V-fold.
-    Irrelevant for Monte-Carlo; default: TRUE \cr
-Default (if NULL): type="MC", V=10, test_size=0.2}
-}
-\if{html}{\out{</div>}}
-}
-}
-\if{html}{\out{<hr>}}
-\if{html}{\out{<a id="method-predict"></a>}}
-\if{latex}{\out{\hypertarget{method-predict}{}}}
-\subsection{Method \code{predict()}}{
-Predict an agghoo model (after calling fit())
-\subsection{Usage}{
-\if{html}{\out{<div class="r">}}\preformatted{AgghooCV$predict(X)}\if{html}{\out{</div>}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{<div class="arguments">}}
-\describe{
-\item{\code{X}}{Matrix or data.frame to predict}
-}
-\if{html}{\out{</div>}}
-}
-}
-\if{html}{\out{<hr>}}
-\if{html}{\out{<a id="method-getParams"></a>}}
-\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{<div class="r">}}\preformatted{AgghooCV$getParams()}\if{html}{\out{</div>}}
-}
-
-}
-\if{html}{\out{<hr>}}
-\if{html}{\out{<a id="method-clone"></a>}}
-\if{latex}{\out{\hypertarget{method-clone}{}}}
-\subsection{Method \code{clone()}}{
-The objects of this class are cloneable with this method.
-\subsection{Usage}{
-\if{html}{\out{<div class="r">}}\preformatted{AgghooCV$clone(deep = FALSE)}\if{html}{\out{</div>}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{<div class="arguments">}}
-\describe{
-\item{\code{deep}}{Whether to make a deep clone.}
-}
-\if{html}{\out{</div>}}
-}
-}
-}