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Fig. 4 | BMC Medical Genomics

Fig. 4

From: Identification of contributing genes of Huntington’s disease by machine learning

Fig. 4

The random forest model. a The receiver operating characteristic (ROC) curve showing the performance of the prediction power of the model. b An example of a decision tree in the modeled random forest. A decision tree plots the “If… then” splitting of samples for prediction. The nodes denote the attributes, while the arrows denote the split, which meets a certain criterion. The number in the result box denotes the prediction result of the model (1 = HD; 0 = control), and the bar denotes the actual sample disease characteristic, bar sickness for sample size and bar segment for the proportion of HD samples (red = HD, blue = control). c The sample distribution in the 3-dimensional eigenspace of gene profiling. Red = HD, blue = control

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