X-Git-Url: https://git.auder.net/?p=agghoo.git;a=blobdiff_plain;f=man%2Fagghoo.Rd;h=179d3097e879c3448791e663876834b032208c16;hp=69dafed8e1c526fb422c437bdb2c2bb0c82cf039;hb=504afaadc783916dc126fb87ab9e067f302eb2c5;hpb=15f48abea9c631d52317ff157c8af0dd4c7a67d3

diff --git a/man/agghoo.Rd b/man/agghoo.Rd
index 69dafed..179d309 100644
--- a/man/agghoo.Rd
+++ b/man/agghoo.Rd
@@ -4,12 +4,13 @@
 \alias{agghoo}
 \title{agghoo}
 \usage{
-agghoo(data, target, task = NULL, gmodel = NULL, params = NULL, quality = NULL)
+agghoo(data, target, task = NULL, gmodel = NULL, params = NULL, loss = NULL)
 }
 \arguments{
 \item{data}{Data frame or matrix containing the data in lines.}
 
-\item{target}{The target values to predict. Generally a vector.}
+\item{target}{The target values to predict. Generally a vector,
+but possibly a matrix in the case of "soft classification".}
 
 \item{task}{"classification" or "regression". Default:
 regression if target is numerical, classification otherwise.}
@@ -24,15 +25,17 @@ of 'param's. See params argument. Default: see R6::Model.}
 numerical value, but in general it could be of any type.
 Default: see R6::Model.}
 
-\item{quality}{A function assessing the quality of a prediction.
+\item{loss}{A function assessing the error of a prediction.
 Arguments are y1 and y2 (comparing a prediction to known values).
-Default: see R6::AgghooCV.}
+loss(y1, y2) --> real number (error). Default: see R6::AgghooCV.}
 }
 \value{
-An R6::AgghooCV object.
+An R6::AgghooCV object o. Then, call o$fit() and finally o$predict(newData)
 }
 \description{
-Run the agghoo procedure. (...)
+Run the agghoo procedure (or standard cross-validation).
+Arguments specify the list of models, their parameters and the
+cross-validation settings, among others.
 }
 \examples{
 # Regression:
@@ -41,7 +44,11 @@ a_reg$fit()
 pr <- a_reg$predict(iris[,-c(2,5)] + rnorm(450, sd=0.1))
 # Classification
 a_cla <- agghoo(iris[,-5], iris[,5])
-a_cla$fit(mode="standard")
+a_cla$fit()
 pc <- a_cla$predict(iris[,-5] + rnorm(600, sd=0.1))
 
 }
+\references{
+Guillaume Maillard, Sylvain Arlot, Matthieu Lerasle. "Aggregated hold-out".
+Journal of Machine Learning Research 22(20):1--55, 2021.
+}