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)}\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$fit(CV = NULL)}\if{html}{\out{</div>}}
  52 }
  53
  54 \subsection{Arguments}{
  55 \if{html}{\out{<div class="arguments">}}
  56 \describe{
  57 \item{\code{CV}}{List describing cross-validation to run. Slots: \cr
  58   - type: 'vfold' or 'MC' for Monte-Carlo (default: MC) \cr
  59   - V: number of runs (default: 10) \cr
  60   - test_size: percentage of data in the test dataset, for MC
  61     (irrelevant for V-fold). Default: 0.2. \cr
  62   - shuffle: wether or not to shuffle data before V-fold.
  63     Irrelevant for Monte-Carlo; default: TRUE \cr
  64 Default (if NULL): type="MC", V=10, test_size=0.2}
  65 }
  66 \if{html}{\out{</div>}}
  67 }
  68 }
  69 \if{html}{\out{<hr>}}
  70 \if{html}{\out{<a id="method-predict"></a>}}
  71 \if{latex}{\out{\hypertarget{method-predict}{}}}
  72 \subsection{Method \code{predict()}}{
  73 Predict an agghoo model (after calling fit())
  74 \subsection{Usage}{
  75 \if{html}{\out{<div class="r">}}\preformatted{AgghooCV$predict(X)}\if{html}{\out{</div>}}
  76 }
  77
  78 \subsection{Arguments}{
  79 \if{html}{\out{<div class="arguments">}}
  80 \describe{
  81 \item{\code{X}}{Matrix or data.frame to predict}
  82 }
  83 \if{html}{\out{</div>}}
  84 }
  85 }
  86 \if{html}{\out{<hr>}}
  87 \if{html}{\out{<a id="method-getParams"></a>}}
  88 \if{latex}{\out{\hypertarget{method-getParams}{}}}
  89 \subsection{Method \code{getParams()}}{
  90 Return the list of V best parameters (after calling fit())
  91 \subsection{Usage}{
  92 \if{html}{\out{<div class="r">}}\preformatted{AgghooCV$getParams()}\if{html}{\out{</div>}}
  93 }
  94
  95 }
  96 \if{html}{\out{<hr>}}
  97 \if{html}{\out{<a id="method-clone"></a>}}
  98 \if{latex}{\out{\hypertarget{method-clone}{}}}
  99 \subsection{Method \code{clone()}}{
 100 The objects of this class are cloneable with this method.
 101 \subsection{Usage}{
 102 \if{html}{\out{<div class="r">}}\preformatted{AgghooCV$clone(deep = FALSE)}\if{html}{\out{</div>}}
 103 }
 104
 105 \subsection{Arguments}{
 106 \if{html}{\out{<div class="arguments">}}
 107 \describe{
 108 \item{\code{deep}}{Whether to make a deep clone.}
 109 }
 110 \if{html}{\out{</div>}}
 111 }
 112 }
 113 }