Constant features can lead to errors in some models and obviously provide no information in the training set that can be learned from. With the argument “perc”, there is a possibility to also remove features for which less than “perc” percent of the observations differ from the mode value.

removeConstantFeatures(
obj,
perc = 0,
dont.rm = character(0L),
na.ignore = FALSE,

## Value

data.frame | Task. Same type as obj.

Other eda_and_preprocess: capLargeValues(), createDummyFeatures(), dropFeatures(), mergeSmallFactorLevels(), normalizeFeatures(), summarizeColumns(), summarizeLevels()