In mlr-org/mlr: Machine Learning in R

library("mlr")
library("BBmisc")
library("ParamHelpers")
urlContribPackages = "https://cran.r-project.org/package="
library("pander")

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

The following table shows the available methods for calculating the feature importance. Columns Classif, Regr and Surv indicate if classification, regression or survival analysis problems are supported. Columns Fac., Num. and Ord. show if a particular method can deal with factor, numeric and ordered factor features.

Current methods

# urlContribPackages is defined in build
linkPkg = function(x) {
  ifelse(x == "", "", collapse(sprintf("[%1$s](%2$s%1$s)", x, urlContribPackages), sep = "<br />"))
}
df = listFilterMethods(desc = TRUE, tasks = TRUE, features = TRUE, include.deprecated = TRUE)
df$package = sapply(df$package, linkPkg)

depr = df$deprecated
df$deprecated = NULL

logicals = vlapply(df, is.logical)
df[logicals] = lapply(df[logicals], function(x) ifelse(x, "X", ""))
df = df[, names(df) %nin% c("feature.integer", "feature.character", "feature.logical")]
names(df) = c("Method", "Package", "Description", "Classif", "Regr", "Surv", "Fac.", "Num.", "Ord.")
just = rep(c("left", "center"), c(3, ncol(df) - 3))
dfnd = df[!depr, ]
rownames(dfnd) = seq_len(nrow(dfnd))
pandoc.table(dfnd, style = "rmarkdown", split.tables = Inf, split.cells = Inf, emphasize.rownames = FALSE, justify = just)

Ensemble methods

df_ens = listFilterEnsembleMethods(desc = TRUE)
colnames(df_ens) = c("Name", "Description")
# rownames(df_ens) = seq_len(nrow(df_ens))
# just = rep(c("left"), c(ncol(df)))
pandoc.table(df_ens, style = "rmarkdown",
  split.tables = Inf, split.cells = Inf,
  justify = "left")

Deprecated methods

dfd = df[depr, ]
rownames(dfd) = seq_len(nrow(dfd))
pandoc.table(dfd, style = "rmarkdown", split.tables = Inf, split.cells = Inf, justify = just)

mlr-org/mlr documentation built on Jan. 12, 2023, 5:16 a.m.