Nonparametric Missing Value Imputation using Random Forest
Nonparametric Missing Value Imputation using Random Forest (ranger by ...
Nonparametric Missing Value Imputation using Random Forests (ranger or...
Compute Imputation Error for Mixed-type Data
Normalized root mean squared error
Introduce Missing Values Completely at Random (MCAR)
Extract Variable Types from a Data Frame
The function 'missForest' in this package is used to impute missing values particularly in the case of mixed-type data. It uses a random forest (via 'ranger' or 'randomForest') trained on the observed values of a data matrix to predict the missing values. It can be used to impute continuous and/or categorical data including complex interactions and non-linear relations. It yields an out-of-bag (OOB) imputation error estimate without the need of a test set or elaborate cross-validation. It can be run in parallel to save computation time.
Useful links