mlr (version 2.3)

impute: Impute and re-impute data

Description

Allows imputation of missing feature values through various techniques. Note that you have the possibility to re-impute a data set in the same way as the imputation was performed during training. This especially comes in handy during resampling when one wants to perform the same imputation on the test set as on the training set.

The function impute performs the imputation on a data set and returns, alongside with the imputed data set, an ImputationDesc object which can contain learned coefficients and helpful data. It can then be passed together with a new data set to reimpute.

The imputation techniques can be specified for certain features or for feature classes, see function arguments.

You can either provide an arbitrary object, use a built-in imputation method listed under imputations or create one yourself using makeImputeMethod.

Usage

impute(data, target = character(0L), classes = list(), cols = list(),
  dummy.classes = character(0L), dummy.cols = character(0L),
  dummy.type = "factor", force.dummies = FALSE, impute.new.levels = TRUE,
  recode.factor.levels = TRUE)

Value

[list]data [data.frame]Imputed data.desc [ImputationDesc]Description object.

Details

The description object contains these slots [object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

See Also

Other impute: imputations, imputeConstant, imputeHist, imputeLearner, imputeMax, imputeMean, imputeMedian, imputeMin, imputeMode, imputeNormal, imputeUniform; makeImputeMethod; makeImputeWrapper; reimpute

Examples

df = data.frame(x = c(1, 1, NA), y = factor(c("a", "a", "b")), z = 1:3)
imputed = impute(df, target = character(0), cols = list(x = 99, y = imputeMode()))
print(imputed$data)
reimpute(data.frame(x = NA), imputed$desc)