The impact of imputation quality on machine learning classifiers for datasets with missing values.

ABSTRACT:

Background

Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missing values are typically imputed using established methods, followed by classification of the now complete samples. The focus of the machine learning researcher is to optimise the classifier's performance.

Methods

We utilise three simulated and three real-world clinical datasets with different feature types and missingness patterns. Initially, we evaluate how the downstream classifier performance depends on the choice of classifier and imputation methods. We employ ANOVA to quantitatively evaluate how the choice of missingness rate, imputation method, and classifier method influences the

SUBMITTER: Shadbahr T 

PROVIDER: S-EPMC10558448 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature