Missing Data Imputation for Supervised Learning

Understand

Missing data imputation can help improve the performance of prediction models in situations where missing data hide useful information.

• This paper compares methods for imputing missing categorical data for supervised classification tasks.
• We experiment on two machine learning benchmark datasets with missing categorical data, comparing classifiers trained on non-imputed (i.e., one-hot encoded) or imputed data with different levels of additional missing-data perturbation.
• We show imputation methods can increase predictive accuracy in the presence of missing-data perturbation, which can actually improve prediction accuracy by regularizing the classifier.