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 using`min(x) - multiplier * diff(range(x))`

,`imputeMax(multiplier)`

for imputing constant values shifted above the maximum using`max(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 using`table`

or`hist`

.`imputeLearner(learner, features = NULL)`

for imputations using the response of a classification or regression learner.

`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)

- 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)`

)

If`x`

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 in`learner`

for prediction. Default is`NULL`

which uses all available features except the target column of the original task.

Other impute:
`impute()`

,
`makeImputeMethod()`

,
`makeImputeWrapper()`

,
`reimpute()`