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Table 1 Summary of predictive accuracy of random forest supervised learning models

From: Forensic analysis of the microbiome of phones and shoes

Sample subset Predicted category N Estimated error ± SD Baseline error Ratio
All phone samples Person 104 0.037 ± 0.062 0.500 13.63
All shoe samples Person 211 0.010 ± 0.020 0.479 50.26
P1 phone samples Front/back 52 0.417 ± 0.206 0.481 1.15
P2 phone samples Front/back 52 0.268 ± 0.180 0.481 1.79
P1 shoe samples Shoe surface 110 0.705 ± 0.125 0.736 1.05
P2 shoe samples Shoe surface 101 0.796 ± 0.090 0.732 0.92
  1. Tenfold cross validation models were constructed with 1,000 trees using OTUs from evenly rarified samples as predictors of sample origin. P1, person 1; P2, person 2, SD, standard deviation, N, number.