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