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Table 2 Some classification algorithms perform better than others, but all yield similar conclusions

From: Extrapolating histone marks across developmental stages, tissues, and species: an enhancer prediction case study

 

Heart features

Non-heart features

Random forest

0.85

0.72

Linear SVM

0.84

0.73

AdaBoost

0.82

0.70

Naive bayes

0.79

0.69

Decision tree

0.77

0.62

KNN (k=3)

0.74

0.66

  1. This table gives ROC AUCs (averaged over five cross-validation folds) for six common algorithms at distinguishing E11.5 heart enhancers from other enhancers based on marks from heart or non-heart tissues.
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BMC Genomics

ISSN: 1471-2164

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