X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=man%2FAgghooCV.Rd;h=97d4c41eb273adb5e851b78d382e46613ee9da51;hb=a7ec4f8a3987ee66daef8471ed1a7a609a987914;hp=e37224b3504919ffbf85444161593e1ecf820bd4;hpb=504afaadc783916dc126fb87ab9e067f302eb2c5;p=agghoo.git

diff --git a/man/AgghooCV.Rd b/man/AgghooCV.Rd
index e37224b..97d4c41 100644
--- a/man/AgghooCV.Rd
+++ b/man/AgghooCV.Rd
@@ -7,13 +7,6 @@
 Class encapsulating the methods to run to obtain the best predictor
 from the list of models (see 'Model' class).
 }
-\section{Public fields}{
-\if{html}{\out{<div class="r6-fields">}}
-\describe{
-\item{\code{params}}{List of parameters of the V selected models}
-}
-\if{html}{\out{</div>}}
-}
 \section{Methods}{
 \subsection{Public methods}{
 \itemize{
@@ -30,7 +23,7 @@ from the list of models (see 'Model' class).
 \subsection{Method \code{new()}}{
 Create a new AgghooCV object.
 \subsection{Usage}{
-\if{html}{\out{<div class="r">}}\preformatted{AgghooCV$new(data, target, task, gmodel, loss = NULL)}\if{html}{\out{</div>}}
+\if{html}{\out{<div class="r">}}\preformatted{AgghooCV$new(data, target, task, gmodel, loss)}\if{html}{\out{</div>}}
 }
 
 \subsection{Arguments}{
@@ -40,11 +33,14 @@ Create a new AgghooCV object.
 
 \item{\code{target}}{Vector of targets (generally numeric or factor)}
 
-\item{\code{task}}{"regression" or "classification"}
+\item{\code{task}}{"regression" or "classification".
+Default: classification if target not numeric.}
 
-\item{\code{gmodel}}{Generic model returning a predictive function}
+\item{\code{gmodel}}{Generic model returning a predictive function
+Default: tree if mixed data, knn/ppr otherwise.}
 
-\item{\code{loss}}{Function assessing the error of a prediction}
+\item{\code{loss}}{Function assessing the error of a prediction
+Default: error rate or mean(abs(error)).}
 }
 \if{html}{\out{</div>}}
 }
@@ -55,19 +51,20 @@ Create a new AgghooCV object.
 \subsection{Method \code{fit()}}{
 Fit an agghoo model.
 \subsection{Usage}{
-\if{html}{\out{<div class="r">}}\preformatted{AgghooCV$fit(CV = list(type = "MC", V = 10, test_size = 0.2, shuffle = TRUE))}\if{html}{\out{</div>}}
+\if{html}{\out{<div class="r">}}\preformatted{AgghooCV$fit(CV = NULL)}\if{html}{\out{</div>}}
 }
 
 \subsection{Arguments}{
 \if{html}{\out{<div class="arguments">}}
 \describe{
-\item{\code{CV}}{List describing cross-validation to run. Slots:
-- type: 'vfold' or 'MC' for Monte-Carlo (default: MC)
-- V: number of runs (default: 10)
-- test_size: percentage of data in the test dataset, for MC
-  (irrelevant for V-fold). Default: 0.2.
-- shuffle: wether or not to shuffle data before V-fold.
-  Irrelevant for Monte-Carlo; default: TRUE}
+\item{\code{CV}}{List describing cross-validation to run. Slots: \cr
+  - type: 'vfold' or 'MC' for Monte-Carlo (default: MC) \cr
+  - V: number of runs (default: 10) \cr
+  - test_size: percentage of data in the test dataset, for MC
+    (irrelevant for V-fold). Default: 0.2. \cr
+  - shuffle: wether or not to shuffle data before V-fold.
+    Irrelevant for Monte-Carlo; default: TRUE \cr
+Default (if NULL): type="MC", V=10, test_size=0.2}
 }
 \if{html}{\out{</div>}}
 }