The built-ins are:
imputeConstant(const)
for imputation using a constant value,imputeMedian()
for imputation using the median,imputeMode()
for imputation using the mode,imputeMin(multiplier)
for imputing constant values shifted below the minimum usingmin(x) - multiplier * diff(range(x))
,imputeMax(multiplier)
for imputing constant values shifted above the maximum usingmax(x) + multiplier * diff(range(x))
,imputeNormal(mean, sd)
for imputation using normally distributed random values. Mean and standard deviation will be calculated from the data if not provided.imputeHist(breaks, use.mids)
for imputation using random values with probabilities calculated usingtable
orhist
.imputeLearner(learner, features = NULL)
for imputations using the response of a classification or regression learner.
Usage
imputeConstant(const)
imputeMedian()
imputeMean()
imputeMode()
imputeMin(multiplier = 1)
imputeMax(multiplier = 1)
imputeUniform(min = NA_real_, max = NA_real_)
imputeNormal(mu = NA_real_, sd = NA_real_)
imputeHist(breaks, use.mids = TRUE)
imputeLearner(learner, features = NULL)
Arguments
- const
(any)
Constant valued use for imputation.- multiplier
(
numeric(1)
)
Value that stored minimum or maximum is multiplied with when imputation is done.- min
(
numeric(1)
)
Lower bound for uniform distribution. If NA (default), it will be estimated from the data.- max
(
numeric(1)
)
Upper bound for uniform distribution. If NA (default), it will be estimated from the data.- mu
(
numeric(1)
)
Mean of normal distribution. If missing it will be estimated from the data.- sd
(
numeric(1)
)
Standard deviation of normal distribution. If missing it will be estimated from the data.- breaks
(
numeric(1)
)
Number of breaks to use in graphics::hist. If missing, defaults to auto-detection via “Sturges”.- use.mids
(
logical(1)
)
Ifx
is numeric and a histogram is used, impute with bin mids (default) or instead draw uniformly distributed samples within bin range.- learner
(Learner |
character(1)
)
Supervised learner. Its predictions will be used for imputations. If you pass a string the learner will be created via makeLearner. Note that the target column is not available for this operation.- features
(character)
Features to use inlearner
for prediction. Default isNULL
which uses all available features except the target column of the original task.
See also
Other impute:
impute()
,
makeImputeMethod()
,
makeImputeWrapper()
,
reimpute()