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Fig. 2 | BMC Medical Genomics

Fig. 2

From: A random forest based biomarker discovery and power analysis framework for diagnostics research

Fig. 2

Results from the simulation study in RF regression mode. a The structure of the simulated predictor data from uniform distribution and the association with outcome variable (y) is described. Only V1-V120 are shown of full dataset featuring 5000 variables. b The number of features stably selected by each approach in at least 5/100 iterations (Low Stringency) or a minimum of 90/100 iterations (High Stringency) are shown. True positive: V1–V30, False positive: V3–V5000. Values describing the number of times each feature is chosen by a particular approach are averaged across those achieved after 100 iterations for each of the four inner loop test datasets. c The variance in predictive accuracy (R-Squared), across all four outer loop cross-validation repeats, is shown for RFs trained using only the high or LS stable features selected by each feature selection approach using the relevant inner loop test dataset

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