createSklearnIterativeImputer: Create scikit-learn Iterative Imputer settings

In PatientLevelPrediction: Develop Clinical Prediction Models Using the Common Data Model

Create scikit-learn Iterative Imputer settings

Description

This function creates settings for a dense iterative imputer powered by scikit-learn's IterativeImputer through reticulate.

Usage

createSklearnIterativeImputer(
  missingThreshold = 0.3,
  methodSettings = list(),
  addMissingIndicator = FALSE
)

Arguments

missingThreshold	The threshold for missing values to remove a feature
methodSettings	A list of settings for sklearn IterativeImputer. Supported settings are: maxIter tol samplePosterior nNearestFeatures initialStrategy imputationOrder skipComplete randomState minValue maxValue
addMissingIndicator	Add a binary missingness indicator per feature that passes the imputation missingness threshold.

Value

The settings for the sklearn iterative imputer of class featureEngineeringSettings

Examples

## Not run: 
createSklearnIterativeImputer(
  missingThreshold = 0.3,
  methodSettings = list(maxIter = 5, nNearestFeatures = 20)
)

## End(Not run)

PatientLevelPrediction documentation built on March 9, 2026, 5:07 p.m.