BMC Bioinformatics

Table 1 Effect of Imputation on Classifier Performance

From: Machine learning with the TCGA-HNSC dataset: improving usability by addressing inconsistency, sparsity, and high-dimensionality

Classifier	Dataset	AUC
Naïve Bayes	Pre-imputation	0.633 ± 0.077
Naïve Bayes	Post-imputation	0.675 ± 0.063
Random Forest	Pre-imputation	0.668 ± 0.062
Random Forest	Post-imputation	0.675 ± 0.063

Classifier performance on the imputed and non-imputed datasets. Baseline AUC is 0.500

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ISSN: 1471-2105

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