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$predict()}}
  16 \item \href{#method-clone}{\code{AgghooCV$clone()}}
  17 }
  18 }
  19 \if{html}{\out{<hr>}}
  20 \if{html}{\out{<a id="method-new"></a>}}
  21 \if{latex}{\out{\hypertarget{method-new}{}}}
  22 \subsection{Method \code{new()}}{
  23 Create a new AgghooCV object.
  24 \subsection{Usage}{
  25 \if{html}{\out{<div class="r">}}\preformatted{AgghooCV$new(data, target, task, gmodel, quality = NA)}\if{html}{\out{</div>}}
  26 }
  27
  28 \subsection{Arguments}{
  29 \if{html}{\out{<div class="arguments">}}
  30 \describe{
  31 \item{\code{data}}{Matrix or data.frame}
  32
  33 \item{\code{target}}{Vector of targets (generally numeric or factor)}
  34
  35 \item{\code{task}}{"regression" or "classification"}
  36
  37 \item{\code{gmodel}}{Generic model returning a predictive function}
  38
  39 \item{\code{quality}}{Function assessing the quality of a prediction;
  40 quality(y1, y2) --> real number}
  41 }
  42 \if{html}{\out{</div>}}
  43 }
  44 }
  45 \if{html}{\out{<hr>}}
  46 \if{html}{\out{<a id="method-fit"></a>}}
  47 \if{latex}{\out{\hypertarget{method-fit}{}}}
  48 \subsection{Method \code{fit()}}{
  49 Fit an agghoo model.
  50 \subsection{Usage}{
  51 \if{html}{\out{<div class="r">}}\preformatted{AgghooCV$fit(
  52   CV = list(type = "MC", V = 10, test_size = 0.2, shuffle = TRUE),
  53   mode = "agghoo"
  54 )}\if{html}{\out{</div>}}
  55 }
  56
  57 \subsection{Arguments}{
  58 \if{html}{\out{<div class="arguments">}}
  59 \describe{
  60 \item{\code{CV}}{List describing cross-validation to run. Slots:
  61 - type: 'vfold' or 'MC' for Monte-Carlo (default: MC)
  62 - V: number of runs (default: 10)
  63 - test_size: percentage of data in the test dataset, for MC
  64   (irrelevant for V-fold). Default: 0.2.
  65 - shuffle: wether or not to shuffle data before V-fold.
  66   Irrelevant for Monte-Carlo; default: TRUE}
  67
  68 \item{\code{mode}}{"agghoo" or "standard" (for usual cross-validation)}
  69 }
  70 \if{html}{\out{</div>}}
  71 }
  72 }
  73 \if{html}{\out{<hr>}}
  74 \if{html}{\out{<a id="method-predict"></a>}}
  75 \if{latex}{\out{\hypertarget{method-predict}{}}}
  76 \subsection{Method \code{predict()}}{
  77 Predict an agghoo model (after calling fit())
  78 \subsection{Usage}{
  79 \if{html}{\out{<div class="r">}}\preformatted{AgghooCV$predict(X, weight = "uniform")}\if{html}{\out{</div>}}
  80 }
  81
  82 \subsection{Arguments}{
  83 \if{html}{\out{<div class="arguments">}}
  84 \describe{
  85 \item{\code{X}}{Matrix or data.frame to predict}
  86
  87 \item{\code{weight}}{"uniform" (default) or "quality" to weight votes or
  88 average models performances (TODO: bad idea?!)}
  89 }
  90 \if{html}{\out{</div>}}
  91 }
  92 }
  93 \if{html}{\out{<hr>}}
  94 \if{html}{\out{<a id="method-clone"></a>}}
  95 \if{latex}{\out{\hypertarget{method-clone}{}}}
  96 \subsection{Method \code{clone()}}{
  97 The objects of this class are cloneable with this method.
  98 \subsection{Usage}{
  99 \if{html}{\out{<div class="r">}}\preformatted{AgghooCV$clone(deep = FALSE)}\if{html}{\out{</div>}}
 100 }
 101
 102 \subsection{Arguments}{
 103 \if{html}{\out{<div class="arguments">}}
 104 \describe{
 105 \item{\code{deep}}{Whether to make a deep clone.}
 106 }
 107 \if{html}{\out{</div>}}
 108 }
 109 }
 110 }