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$set(collapse = TRUE)
The following table shows the available methods for calculating the feature importance.
Columns Classif, Regr and Surv indicate if classification, regression or survival analysis problems are supported.
Columns Fac., Num. and Ord. show if a particular method can deal with factor
, numeric
and ordered factor
features.
# urlContribPackages is defined in build linkPkg = function(x) { ifelse(x == "", "", collapse(sprintf("[%1$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)
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")
dfd = df[depr, ] rownames(dfd) = seq_len(nrow(dfd)) pandoc.table(dfd, style = "rmarkdown", split.tables = Inf, split.cells = Inf, justify = just)
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