Update package to send on CRAN

[agghoo.git] / man / agghoo.Rd

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-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/agghoo.R
-\name{agghoo}
-\alias{agghoo}
-\title{agghoo}
-\usage{
-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,
-but possibly a matrix in the case of "soft classification".}
-
-\item{task}{"classification" or "regression". Default:
-regression if target is numerical, classification otherwise.}
-
-\item{gmodel}{A "generic model", which is a function returning a predict
-function (taking X as only argument) from the tuple
-(dataHO, targetHO, param), where 'HO' stands for 'Hold-Out',
-referring to cross-validation. Cross-validation is run on an array
-of 'param's. See params argument. Default: see R6::Model.}
-
-\item{params}{A list of parameters. Often, one list cell is just a
-numerical value, but in general it could be of any type.
-Default: see R6::Model.}
-
-\item{loss}{A function assessing the error of a prediction.
-Arguments are y1 and y2 (comparing a prediction to known values).
-loss(y1, y2) --> real number (error). Default: see R6::AgghooCV.}
-}
-\value{
-An R6::AgghooCV object o. Then, call o$fit() and finally o$predict(newData)
-}
-\description{
-Run the (core) agghoo procedure.
-Arguments specify the list of models, their parameters and the
-cross-validation settings, among others.
-}
-\examples{
-# Regression:
-a_reg <- agghoo(iris[,-c(2,5)], iris[,2])
-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$fit()
-pc <- a_cla$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}}
-}