Fix typo
[agghoo.git] / man / agghoo.Rd
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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{
7agghoo(data, target, task = NA, gmodel = NA, params = NA, quality = NA)
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
14\item{task}{"classification" or "regression". Default:
15regression if target is numerical, classification otherwise.}
16
17\item{gmodel}{A "generic model", which is a function returning a predict
18function (taking X as only argument) from the tuple
19(dataHO, targetHO, param), where 'HO' stands for 'Hold-Out',
20referring to cross-validation. Cross-validation is run on an array
21of 'param's. See params argument. Default: see R6::Model.}
22
23\item{params}{A list of parameters. Often, one list cell is just a
24numerical value, but in general it could be of any type.
25Default: see R6::Model.}
26
27\item{quality}{A function assessing the quality of a prediction.
28Arguments are y1 and y2 (comparing a prediction to known values).
29Default: see R6::Agghoo.}
30}
31\value{
32An R6::Agghoo object.
33}
34\description{
35Run the agghoo procedure. (...)
36}
37\examples{
38# Regression:
39a_reg <- agghoo(iris[,-c(2,5)], iris[,2])
40a_reg$fit()
41pr <- a_reg$predict(iris[,-c(2,5)] + rnorm(450, sd=0.1))
42# Classification
43a_cla <- agghoo(iris[,-5], iris[,5])
44a_cla$fit(mode="standard")
45pc <- a_cla$predict(iris[,-5] + rnorm(600, sd=0.1))
46
47}