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Mulitple Imputation for Missing Values

Multipe Imputation for Missing Values

This workflow shows an example for multiple imputation using the R “mice” package to create 5 complete datasets. The analysis - in this case training a classification problem - is then run for each complete dataset. In the last step the results are pooled using a majority voting.

Create 5 complete datasetsusing the R package mice Run analysis for each complete dataset Pool the results using majority voting Create mcomplete datasets,with max 10 iterationsOne completedataset per iterationExtract most oftenpredicted classCount predictionsper classAdd columnwith true valuesEvaluateperformanceGenerateidentifier for different rows Census income datasetR Snippet Group Loop Start Partitioning DecisionTree Learner Decision TreePredictor Column Filter Loop End Many to One Pivoting Joiner Scorer Counter Generation DuplicateRow Filter Read data and sprinklemissing values Create 5 complete datasetsusing the R package mice Run analysis for each complete dataset Pool the results using majority voting Create mcomplete datasets,with max 10 iterationsOne completedataset per iterationExtract most oftenpredicted classCount predictionsper classAdd columnwith true valuesEvaluateperformanceGenerateidentifier for different rowsCensus income datasetR Snippet Group Loop Start Partitioning DecisionTree Learner Decision TreePredictor Column Filter Loop End Many to One Pivoting Joiner Scorer Counter Generation DuplicateRow Filter Read data and sprinklemissing values

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

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Created by: kathrinmelcher
Created at: 2020-06-02
On NodePit since: 2020-12-17
Last update: 2025-01-20
Created with KNIME version: v4.2.1
Tags: Missing valuesMultiple Imputation

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