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Ignore doc/ folder
[agghoo.git]
/
man
/
agghoo.Rd
diff --git
a/man/agghoo.Rd
b/man/agghoo.Rd
index
dea76a1
..
38730eb
100644
(file)
--- a/
man/agghoo.Rd
+++ b/
man/agghoo.Rd
@@
-4,12
+4,13
@@
\alias{agghoo}
\title{agghoo}
\usage{
\alias{agghoo}
\title{agghoo}
\usage{
-agghoo(data, target, task = N
A, gmodel = NA, params = NA, quality = NA
)
+agghoo(data, target, task = N
ULL, gmodel = NULL, params = NULL, loss = NULL
)
}
\arguments{
\item{data}{Data frame or matrix containing the data in lines.}
}
\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.}
\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.}
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).
Arguments are y1 and y2 (comparing a prediction to known values).
-
Default: see R6::Agghoo
.}
+
loss(y1, y2) --> real number (error). Default: see R6::AgghooCV
.}
}
\value{
}
\value{
-An R6::Agghoo
object.
+An R6::Agghoo
CV object o. Then, call o$fit() and finally o$predict(newData)
}
\description{
}
\description{
-Run the agghoo procedure. (...)
+Run the (core) agghoo procedure.
+Arguments specify the list of models, their parameters and the
+cross-validation settings, among others.
}
\examples{
# Regression:
}
\examples{
# Regression:
@@
-41,7
+44,14
@@
a_reg$fit()
pr <- a_reg$predict(iris[,-c(2,5)] + rnorm(450, sd=0.1))
# Classification
a_cla <- agghoo(iris[,-5], iris[,5])
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))
}
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.
+}
+\seealso{
+Function \code{\link{compareTo}}
+}