man/AgghooCV.Rd

   1 % Generated by roxygen2: do not edit by hand
   2 % Please edit documentation in R/R6_AgghooCV.R
   3 \name{AgghooCV}
   4 \alias{AgghooCV}
   5 \title{R6 class with agghoo functions fit() and predict().}
   6 \description{
   7 Class encapsulating the methods to run to obtain the best predictor
   8 from the list of models (see 'Model' class).
   9 }
  10 \section{Methods}{
  11 \subsection{Public methods}{
  12 \itemize{
  13 \item \href{#method-new}{\code{AgghooCV$new()}}
  14 \item \href{#method-fit}{\code{AgghooCV$fit()}}
  15 \item \href{#method-predict}{\code{AgghooCV$predict()}}
  16 \item \href{#method-getParams}{\code{AgghooCV$getParams()}}
  17 \item \href{#method-clone}{\code{AgghooCV$clone()}}
  18 }
  19 }
  20 \if{html}{\out{<hr>}}
  21 \if{html}{\out{<a id="method-new"></a>}}
  22 \if{latex}{\out{\hypertarget{method-new}{}}}
  23 \subsection{Method \code{new()}}{
  24 Create a new AgghooCV object.
  25 \subsection{Usage}{
  26 \if{html}{\out{<div class="r">}}\preformatted{AgghooCV$new(data, target, task, gmodel, loss = NULL)}\if{html}{\out{</div>}}
  27 }
  28
  29 \subsection{Arguments}{
  30 \if{html}{\out{<div class="arguments">}}
  31 \describe{
  32 \item{\code{data}}{Matrix or data.frame}
  33
  34 \item{\code{target}}{Vector of targets (generally numeric or factor)}
  35
  36 \item{\code{task}}{"regression" or "classification"}
  37
  38 \item{\code{gmodel}}{Generic model returning a predictive function}
  39
  40 \item{\code{loss}}{Function assessing the error of a prediction}
  41 }
  42 \if{html}{\out{</div>}}
  43 }
  44 }
  45 \if{html}{\out{<hr>}}
  46 \if{html}{\out{<a id="method-fit"></a>}}
  47 \if{latex}{\out{\hypertarget{method-fit}{}}}
  48 \subsection{Method \code{fit()}}{
  49 Fit an agghoo model.
  50 \subsection{Usage}{
  51 \if{html}{\out{<div class="r">}}\preformatted{AgghooCV$fit(CV = list(type = "MC", V = 10, test_size = 0.2, shuffle = TRUE))}\if{html}{\out{</div>}}
  52 }
  53
  54 \subsection{Arguments}{
  55 \if{html}{\out{<div class="arguments">}}
  56 \describe{
  57 \item{\code{CV}}{List describing cross-validation to run. Slots: \cr
  58 - type: 'vfold' or 'MC' for Monte-Carlo (default: MC) \cr
  59 - V: number of runs (default: 10) \cr
  60 - test_size: percentage of data in the test dataset, for MC \cr
  61   (irrelevant for V-fold). Default: 0.2.
  62 - shuffle: wether or not to shuffle data before V-fold.
  63   Irrelevant for Monte-Carlo; default: TRUE}
  64 }
  65 \if{html}{\out{</div>}}
  66 }
  67 }
  68 \if{html}{\out{<hr>}}
  69 \if{html}{\out{<a id="method-predict"></a>}}
  70 \if{latex}{\out{\hypertarget{method-predict}{}}}
  71 \subsection{Method \code{predict()}}{
  72 Predict an agghoo model (after calling fit())
  73 \subsection{Usage}{
  74 \if{html}{\out{<div class="r">}}\preformatted{AgghooCV$predict(X)}\if{html}{\out{</div>}}
  75 }
  76
  77 \subsection{Arguments}{
  78 \if{html}{\out{<div class="arguments">}}
  79 \describe{
  80 \item{\code{X}}{Matrix or data.frame to predict}
  81 }
  82 \if{html}{\out{</div>}}
  83 }
  84 }
  85 \if{html}{\out{<hr>}}
  86 \if{html}{\out{<a id="method-getParams"></a>}}
  87 \if{latex}{\out{\hypertarget{method-getParams}{}}}
  88 \subsection{Method \code{getParams()}}{
  89 Return the list of V best parameters (after calling fit())
  90 \subsection{Usage}{
  91 \if{html}{\out{<div class="r">}}\preformatted{AgghooCV$getParams()}\if{html}{\out{</div>}}
  92 }
  93
  94 }
  95 \if{html}{\out{<hr>}}
  96 \if{html}{\out{<a id="method-clone"></a>}}
  97 \if{latex}{\out{\hypertarget{method-clone}{}}}
  98 \subsection{Method \code{clone()}}{
  99 The objects of this class are cloneable with this method.
 100 \subsection{Usage}{
 101 \if{html}{\out{<div class="r">}}\preformatted{AgghooCV$clone(deep = FALSE)}\if{html}{\out{</div>}}
 102 }
 103
 104 \subsection{Arguments}{
 105 \if{html}{\out{<div class="arguments">}}
 106 \describe{
 107 \item{\code{deep}}{Whether to make a deep clone.}
 108 }
 109 \if{html}{\out{</div>}}
 110 }
 111 }
 112 }