A Methylation Diagnostic Model Based on Random Forests and Neural Networks for Asthma Identification

Figure 4

(a) The effect of the number of decision trees on the error rate. The -axis was the number of decision trees, and the -axis was the error rate. The increase of trees did not affect the reduction of the error rate. (b) After the variables were entered into the random forest, the top 10 DECs were listed in order of importance according to MeanDecreaseAccuracy (left) and MeanDecreaseGini (right). (c) Hierarchical clustering results of 10 DECs in GSE85566 dataset; dark colors represent high expression, light colors represent low expression, the red band above the heat map represents normal samples, and green represents asthma samples.

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