Feature Importance Plot

Creates a feature importance plot.

plotFeatureImportance(featureList, control = list(), ...)

Arguments

featureList	[list] List of vectors of features. One list element is expected to belong to one resampling iteration / fold.
control	[list] A list, which stores additional configuration parameters: featimp.col_{high/medium/low}: Color of the features, which are used often, sometimes or only a few times. featimp.perc_{high/low}: Percentage of the total number of folds, defining when a features, is used often, sometimes or only a few times. featimp.las: Alignment of axis labels. featimp.{xlab/ylab}: Axis labels. featimp.string_angle: Angle for the features on the x-axis. featimp.pch_{active/inactive}: Plot symbol of the active and inactive points. featimp.col_inactive: Color of the inactive points. featimp.col_vertical: Color of the vertical lines.
...	[any] Further arguments, which can be passed to plot.

Value

[plot]. Feature Importance Plot, indicating which feature was used during which iteration.

Examples

not_run({
  # At the beginning, one needs a list of features, e.g. derived during a
  # nested feature selection within mlr (see the following 8 steps):
  library(mlr)
  library(mlbench)
  data(Glass)

  # (1) Create a classification task:
  classifTask = makeClassifTask(data = Glass, target = "Type")

  # (2) Define the model (here, a classification tree):
  lrn = makeLearner(cl = "classif.rpart")

  # (3) Define the resampling strategy, which is supposed to be used within 
  # each inner loop of the nested feature selection:
  innerResampling = makeResampleDesc("Holdout")

  # (4) What kind of feature selection approach should be used? Here, we use a
  # sequential backward strategy, i.e. starting from a model with all features,
  # in each step the feature decreasing the performance measure the least is
  # removed from the model:
  ctrl = makeFeatSelControlSequential(method = "sbs")

  # (5) Wrap the original model (see (2)) in order to allow feature selection:
  wrappedLearner = makeFeatSelWrapper(learner = lrn,
    resampling = innerResampling, control = ctrl)

  # (6) Define a resampling strategy for the outer loop. This is necessary in
  # order to assess whether the selected features depend on the underlying
  # fold:
  outerResampling = makeResampleDesc(method = "CV", iters = 10L)

  # (7) Perform the feature selection:
  featselResult = resample(learner = wrappedLearner, task = classifTask,
    resampling = outerResampling, models = TRUE)

  # (8) Extract the features, which were selected during each iteration of the
  # outer loop (i.e. during each of the 5 folds of the cross-validation):
  featureList = lapply(featselResult$models,
    function(mod) getFeatSelResult(mod)$x)
})

########################################################################

# Now, one could inspect the features manually:
featureList
#> [[1]]
#> [1] "RI" "Ca" "Ba"
#> 
#> [[2]]
#> [1] "RI" "Mg" "Al" "K"  "Ba"
#> 
#> [[3]]
#> [1] "RI" "Na" "Mg" "K"  "Ba"
#> 
#> [[4]]
#> [1] "RI" "Al" "Si" "Ca"
#> 
#> [[5]]
#> [1] "RI" "Na" "Mg" "Ba"
#> 
#> [[6]]
#> [1] "RI" "Mg" "Al" "Ca" "Ba"
#> 
#> [[7]]
#> [1] "Al" "Si" "K"  "Ca" "Ba"
#> 
#> [[8]]
#> [1] "RI" "Na" "Al" "Ca" "Ba"
#> 
#> [[9]]
#> [1] "RI" "Na" "Mg" "Ba"
#> 
#> [[10]]
#> [1] "RI" "Mg" "Ba"
#> 

# Alternatively, one might use visual means such as the feature
# importance plot:
plotFeatureImportance(featureList)