man/agghoo.Rd

   1 % Generated by roxygen2: do not edit by hand
   2 % Please edit documentation in R/agghoo.R
   3 \name{agghoo}
   4 \alias{agghoo}
   5 \title{agghoo}
   6 \usage{
   7 agghoo(data, target, task = NULL, gmodel = NULL, params = NULL, loss = NULL)
   8 }
   9 \arguments{
  10 \item{data}{Data frame or matrix containing the data in lines.}
  11
  12 \item{target}{The target values to predict. Generally a vector,
  13 but possibly a matrix in the case of "soft classification".}
  14
  15 \item{task}{"classification" or "regression". Default:
  16 regression if target is numerical, classification otherwise.}
  17
  18 \item{gmodel}{A "generic model", which is a function returning a predict
  19 function (taking X as only argument) from the tuple
  20 (dataHO, targetHO, param), where 'HO' stands for 'Hold-Out',
  21 referring to cross-validation. Cross-validation is run on an array
  22 of 'param's. See params argument. Default: see R6::Model.}
  23
  24 \item{params}{A list of parameters. Often, one list cell is just a
  25 numerical value, but in general it could be of any type.
  26 Default: see R6::Model.}
  27
  28 \item{loss}{A function assessing the error of a prediction.
  29 Arguments are y1 and y2 (comparing a prediction to known values).
  30 loss(y1, y2) --> real number (error). Default: see R6::AgghooCV.}
  31 }
  32 \value{
  33 An R6::AgghooCV object o. Then, call o$fit() and finally o$predict(newData)
  34 }
  35 \description{
  36 Run the (core) agghoo procedure.
  37 Arguments specify the list of models, their parameters and the
  38 cross-validation settings, among others.
  39 }
  40 \examples{
  41 # Regression:
  42 a_reg <- agghoo(iris[,-c(2,5)], iris[,2])
  43 a_reg$fit()
  44 pr <- a_reg$predict(iris[,-c(2,5)] + rnorm(450, sd=0.1))
  45 # Classification
  46 a_cla <- agghoo(iris[,-5], iris[,5])
  47 a_cla$fit()
  48 pc <- a_cla$predict(iris[,-5] + rnorm(600, sd=0.1))
  49
  50 }
  51 \references{
  52 Guillaume Maillard, Sylvain Arlot, Matthieu Lerasle. "Aggregated hold-out".
  53 Journal of Machine Learning Research 22(20):1--55, 2021.
  54 }
  55 \seealso{
  56 Function \code{\link{compareTo}}
  57 }