Ignore doc/ folder
[agghoo.git] / 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 Default: classification if target not numeric.}
38
39 \item{\code{gmodel}}{Generic model returning a predictive function
40 Default: tree if mixed data, knn/ppr otherwise.}
41
42 \item{\code{loss}}{Function assessing the error of a prediction
43 Default: error rate or mean(abs(error)).}
44 }
45 \if{html}{\out{</div>}}
46 }
47 }
48 \if{html}{\out{<hr>}}
49 \if{html}{\out{<a id="method-fit"></a>}}
50 \if{latex}{\out{\hypertarget{method-fit}{}}}
51 \subsection{Method \code{fit()}}{
52 Fit an agghoo model.
53 \subsection{Usage}{
54 \if{html}{\out{<div class="r">}}\preformatted{AgghooCV$fit(CV = NULL)}\if{html}{\out{</div>}}
55 }
56
57 \subsection{Arguments}{
58 \if{html}{\out{<div class="arguments">}}
59 \describe{
60 \item{\code{CV}}{List describing cross-validation to run. Slots: \cr
61 - type: 'vfold' or 'MC' for Monte-Carlo (default: MC) \cr
62 - V: number of runs (default: 10) \cr
63 - test_size: percentage of data in the test dataset, for MC
64 (irrelevant for V-fold). Default: 0.2. \cr
65 - shuffle: wether or not to shuffle data before V-fold.
66 Irrelevant for Monte-Carlo; default: TRUE \cr
67 Default (if NULL): type="MC", V=10, test_size=0.2}
68 }
69 \if{html}{\out{</div>}}
70 }
71 }
72 \if{html}{\out{<hr>}}
73 \if{html}{\out{<a id="method-predict"></a>}}
74 \if{latex}{\out{\hypertarget{method-predict}{}}}
75 \subsection{Method \code{predict()}}{
76 Predict an agghoo model (after calling fit())
77 \subsection{Usage}{
78 \if{html}{\out{<div class="r">}}\preformatted{AgghooCV$predict(X)}\if{html}{\out{</div>}}
79 }
80
81 \subsection{Arguments}{
82 \if{html}{\out{<div class="arguments">}}
83 \describe{
84 \item{\code{X}}{Matrix or data.frame to predict}
85 }
86 \if{html}{\out{</div>}}
87 }
88 }
89 \if{html}{\out{<hr>}}
90 \if{html}{\out{<a id="method-getParams"></a>}}
91 \if{latex}{\out{\hypertarget{method-getParams}{}}}
92 \subsection{Method \code{getParams()}}{
93 Return the list of V best parameters (after calling fit())
94 \subsection{Usage}{
95 \if{html}{\out{<div class="r">}}\preformatted{AgghooCV$getParams()}\if{html}{\out{</div>}}
96 }
97
98 }
99 \if{html}{\out{<hr>}}
100 \if{html}{\out{<a id="method-clone"></a>}}
101 \if{latex}{\out{\hypertarget{method-clone}{}}}
102 \subsection{Method \code{clone()}}{
103 The objects of this class are cloneable with this method.
104 \subsection{Usage}{
105 \if{html}{\out{<div class="r">}}\preformatted{AgghooCV$clone(deep = FALSE)}\if{html}{\out{</div>}}
106 }
107
108 \subsection{Arguments}{
109 \if{html}{\out{<div class="arguments">}}
110 \describe{
111 \item{\code{deep}}{Whether to make a deep clone.}
112 }
113 \if{html}{\out{</div>}}
114 }
115 }
116 }