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.
perc = 0,
dont.rm = character(0L),
na.ignore = FALSE,
wrap.tol = .Machine$double.eps^0.5,
show.info = getMlrOption("show.info"),
(data.frame | Task)
The percentage of a feature values in [0, 1) that must differ from the mode value.
Default is 0, which means only constant features with exactly one observed level are removed.
Names of the columns which must not be deleted.
Default is no columns.
Should NAs be ignored in the percentage calculation?
(Or should they be treated as a single, extra level in the percentage calculation?)
Note that if the feature has only missing values, it is always removed.
Numerical tolerance to treat two numbers as equal.
Variables stored as
double will get rounded accordingly before computing the mode.
Print verbose output on console?
Default is set via configureMlr.
To ensure backward compatibility with old argument
data.frame | Task. Same type as