In berndbischl/mlr: Machine Learning in R

library("mlr")
library("BBmisc")
library("ParamHelpers")
urlContribPackages = urlBasePackages = "http://www.rdocumentation.org/packages/"

## show grouped code output instead of single lines
knitr::opts_chunk$set(collapse = TRUE)

This page lists the learning methods already integrated in mlr.

Columns Num., Fac., Ord., NAs, and Weights indicate if a method can cope with numerical, factor, and ordered factor predictors, if it can deal with missing values in a meaningful way (other than simply removing observations with missing values) and if observation weights are supported.

Column Props shows further properties of the learning methods specific to the type of learning task. See also RLearner() for details.

library("mlr")
library("pander")
# baseR, urlBasePackages, urlContribPackages are defined in build
linkPkg = function(x) {
  x = strsplit(x, ",")[[1]]
  x = mlr:::cleanupPackageNames(x) # remove exclamation marks
  if (urlBasePackages != urlContribPackages) {
    ind = x %in% baseR
    url = c(urlContribPackages, urlBasePackages)[ind + 1]
  } else {
    url = urlContribPackages
  }
  collapse(sprintf("[%1$s](%2$s%1$s/)", x, url), sep = "<br />")
}

getTab = function(type) {
  cn = function(x) {
    if (is.null(x)) {
      NA
    } else {
      r = gsub("\\n", " ", x)
      gsub("(\\$)", "\\\\\\1", r)
    }
  }
  lrns = listLearners(type)
  lrns$note[is.na(lrns$note)] = ""
  cols = c("class", "type", "package", "short.name", "name", "numerics", "factors", "ordered", "missings", "weights", "note", "installed")
  props = setdiff(colnames(lrns), cols)

  colNames = c("Class / Short Name / Name", "Packages", "Num.", "Fac.", "Ord.", "NAs", "Weights", "Props", "Note")
  df = makeDataFrame(nrow = nrow(lrns), ncol = length(colNames),
    col.types = c("character", "character", "logical", "logical", "logical", "logical", "logical", "character", "character"))
  names(df) = colNames

  df[1] = apply(lrns[c("class", "short.name", "name")], 1, function(x) {
    paste0("**", x["class"], "** <br /> *", cn(x["short.name"]), "* <br /><br />", cn(x["name"]))
  })
  df$Packages = sapply(lrns$package, linkPkg)
  df[3:7] = lrns[c("numerics", "factors", "ordered", "missings", "weights")]
  df$Props = apply(lrns[props], 1, function(x) collapse(props[x], sep = "<br />"))
  df$Note = sapply(lrns$note, cn)
  logicals = vlapply(df, is.logical)
  df[logicals] = lapply(df[logicals], function(x) ifelse(x, "X", ""))
  df
}

makeTab = function(df) {
  pandoc.table(df, style = "rmarkdown", split.tables = Inf, split.cells = Inf,
    justify = c("left", "left", "center", "center", "center", "center", "center", "left", "left"))
}

types = c("classif", "multilabel", "regr", "surv", "cluster")
tables = lapply(types, getTab)
names(tables) = types
numbers = sapply(tables, nrow)

Classification (r numbers["classif"])

For classification the following additional learner properties are relevant and shown in column Props:

• prob: The method can predict probabilities,
• oneclass, twoclass, multiclass: One-class, two-class (binary) or multi-class classification problems be handled,
• class.weights: Class weights can be handled.

makeTab(tables[["classif"]])

Regression (r numbers["regr"])

Additional learner properties:

• se: Standard errors can be predicted.

makeTab(tables[["regr"]])

Survival analysis (r numbers["surv"])

Additional learner properties:

• prob: Probabilities can be predicted,
• rcens, lcens, icens: The learner can handle right, left and/or interval censored data.

makeTab(tables[["surv"]])

Cluster analysis (r numbers["cluster"])

Additional learner properties:

• prob: Probabilities can be predicted.

makeTab(tables[["cluster"]])

Cost-sensitive classification

For ordinary misclassification costs you can use all the standard classification methods listed above.

For example-dependent costs there are several ways to generate cost-sensitive learners from ordinary regression and classification learners. See section cost-sensitive classification{target="_blank"} and the documentation of makeCostSensClassifWrapper(), makeCostSensRegrWrapper() and makeCostSensWeightedPairsWrapper() for details.

Multilabel classification (r numbers["multilabel"])

makeTab(tables[["multilabel"]])

Moreover, you can use the binary relevance method to apply ordinary classification learners to the multilabel problem. See the documentation of function makeMultilabelBinaryRelevanceWrapper() and the tutorial section on multilabel classification{target="_blank"} for details.

berndbischl/mlr documentation built on Jan. 6, 2023, 12:45 p.m.