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)

## 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)` )
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. |

## See also