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Analyzing the Effect of Imputation on Classification Performance under MCAR and MAR Missing Mechanisms
Philip Buczak1, Jian-Jia Chen2, Markus Pauly1,3
1Department of Statistics, TU Dortmund University, 44227 Dortmund, Germany.
Entropy (Basel, Switzerland)
|March 29, 2023
Summary
Random Forest-based imputation methods like MICE Random Forest and missForest excel at handling missing data in binary classification. Simple mean imputation is also effective, particularly with many missing features.
Area of Science:
- Statistical analysis
- Machine learning
- Data science
Background:
- Missing values in datasets can lead to information loss and reduced sample size.
- Imputation is preferred over deletion but can introduce bias and affect analysis validity.
Purpose of the Study:
- To evaluate the impact of missing value imputation on binary classification performance.
- To compare various imputation methods under different missing data mechanisms (MCAR, MAR) and patterns.
Main Methods:
- Compared multiple imputation by chained equations (MICE) variants, missForest, Hot Deck, and mean imputation.
- Assessed imputation performance using classifiers like Random Forest, Extreme Gradient Boosting, Support Vector Machine, and regularized logistic regression.
Main Results:
- Random Forest-based imputation (MICE Random Forest, missForest) showed superior performance across most scenarios.
- Mean imputation was effective, especially when a large number of features had missing values.
Conclusions:
- Random Forest-based imputation methods are highly recommended for binary classification tasks with missing data.
- Mean imputation offers a simple yet effective alternative in specific high-missingness feature scenarios.
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