Additional file 15: of The parameter sensitivity of random forests

Variable importance ranks according to ntree value. The ranks for the default parameters with unique n tree values as column heads and sequencing quality metrics as row heads. The ranks stabilize at n tree = 10,000. Using this criteria, the variable identified as the most important was “Average reads/starts” in 40 % of samples, whereas, [6] identified “% bases ≥ 8×” as the most important variable using the cforest algorithm. Below n tree 1,000, “% bases ≥ 8×” was ranked as the most important variable in 40 % of samples. Although greater n tree values may lead to more consistent rankings for variable importance, these values may become more biased through sampling with replacement methods [20]. (CSV 667 bytes)