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---
title: "Example Tasks"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{mlr}
%\VignetteEngine{knitr::rmarkdown}
\usepackage[utf8]{inputenc}
---
```{r, echo = FALSE, message=FALSE}
library("mlr")
library("BBmisc")
library("ParamHelpers")
urlMlrFunctions = "https://mlr-org.github.io/mlr/reference/"
ext = ".html"
library("pander")
# show grouped code output instead of single lines
knitr::opts_chunk$set(collapse = TRUE)
```
For your convenience `mlr` provides pre-defined `Task()`s for each type of learning problem.
These are used throughout this tutorial in order to get shorter and more readable code.
```{r echo=FALSE, results="asis"}
# urlMlrFunctions, ext are defined in build
linkTask = function(x) {
collapse(sprintf("[%1$s](%2$s%1$s%3$s)", x, urlMlrFunctions, ext), sep = "<br />")
}
d = data(package = "mlr")
d = d$results
types = sapply(d[,"Item"], function(x) getTaskDesc(eval(parse(text = x)))$type)
names(types) = NULL
ord = order(types)
d = d[ord,]
types = types[ord]
types[duplicated(types)] = ""
df = data.frame(Type = types, Task = d[,"Item"], Description = d[,"Title"])
df$Task = sapply(df$Task, linkTask)
pandoc.table(df, style = "rmarkdown", split.tables = Inf, split.cells = Inf,
justify = rep("left", 3))
```
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