Create Iterative Imputer settings

This function creates the settings for an iterative imputer which first removes features with more than missingThreshold missing values and then imputes the missing values iteratively using chained equations

Usage

createIterativeImputer(
  missingThreshold = 0.3,
  method = "pmm",
  methodSettings = list(pmm = list(k = 5, iterations = 5))
)

Arguments

missingThreshold

The threshold for missing values to remove a feature

method

The method to use for imputation, currently only "pmm" is supported

methodSettings

A list of settings for the imputation method to use. Currently only "pmm" is supported with the following settings:

• k: The number of donors to use for matching

• iterations: The number of iterations to use for imputation

Value

The settings for the iterative imputer of class featureEngineeringSettings

Examples

# create imputer to impute values with missingness less than 30% using 
# predictive mean matching in 5 iterations with 5 donors
createIterativeImputer(missingThreshold = 0.3, method = "pmm",
                       methodSettings = list(pmm = list(k = 5, iterations = 5)))
#> $missingThreshold
#> [1] 0.3
#> 
#> $method
#> [1] "pmm"
#> 
#> $methodSettings
#> $methodSettings$k
#> [1] 5
#> 
#> $methodSettings$iterations
#> [1] 5
#> 
#> 
#> attr(,"fun")
#> [1] "iterativeImpute"
#> attr(,"class")
#> [1] "featureEngineeringSettings"