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1% Generated by roxygen2: do not edit by hand
2% Please edit documentation in R/R6_Agghoo.R
3\name{Agghoo}
4\alias{Agghoo}
5\title{R6 class with agghoo functions fit() and predict().}
6\description{
7Class encapsulating the methods to run to obtain the best predictor
8from the list of models (see 'Model' class).
9}
10\section{Methods}{
11\subsection{Public methods}{
12\itemize{
13\item \href{#method-new}{\code{Agghoo$new()}}
14\item \href{#method-fit}{\code{Agghoo$fit()}}
15\item \href{#method-predict}{\code{Agghoo$predict()}}
16\item \href{#method-clone}{\code{Agghoo$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()}}{
23Create a new Agghoo object.
24\subsection{Usage}{
25\if{html}{\out{<div class="r">}}\preformatted{Agghoo$new(data, target, task, gmodel, quality = NA)}\if{html}{\out{</div>}}
26}
27
28\subsection{Arguments}{
29\if{html}{\out{<div class="arguments">}}
30\describe{
31\item{\code{data}}{Matrix or data.frame}
32
33\item{\code{target}}{Vector of targets (generally numeric or factor)}
34
35\item{\code{task}}{"regression" or "classification"}
36
37\item{\code{gmodel}}{Generic model returning a predictive function}
38
39\item{\code{quality}}{Function assessing the quality of a prediction;
40quality(y1, y2) --> real number}
41}
42\if{html}{\out{</div>}}
43}
44}
45\if{html}{\out{<hr>}}
46\if{html}{\out{<a id="method-fit"></a>}}
47\if{latex}{\out{\hypertarget{method-fit}{}}}
48\subsection{Method \code{fit()}}{
49Fit an agghoo model.
50\subsection{Usage}{
51\if{html}{\out{<div class="r">}}\preformatted{Agghoo$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()}}{
77Predict an agghoo model (after calling fit())
78\subsection{Usage}{
79\if{html}{\out{<div class="r">}}\preformatted{Agghoo$predict(X, weight = "uniform")}\if{html}{\out{</div>}}
80}
81
82\subsection{Arguments}{
83\if{html}{\out{<div class="arguments">}}
84\describe{
85\item{\code{X}}{Matrix or data.frame to predict}
86
87\item{\code{weight}}{"uniform" (default) or "quality" to weight votes or
88average models performances (TODO: bad idea?!)}
89}
90\if{html}{\out{</div>}}
91}
92}
93\if{html}{\out{<hr>}}
94\if{html}{\out{<a id="method-clone"></a>}}
95\if{latex}{\out{\hypertarget{method-clone}{}}}
96\subsection{Method \code{clone()}}{
97The objects of this class are cloneable with this method.
98\subsection{Usage}{
99\if{html}{\out{<div class="r">}}\preformatted{Agghoo$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}