Table 1.
Fig 1.
Classification tree algorithm.
The classification tree represents the different selection criteria or ‘decision nodes’ used to predict the most correct classification of the total number of cases (represented at the root of the tree as a 100%). As the data is classified in subsets, the percentage value represents the probability of a case of belonging to that data subset.
Fig 2.
Variable importance plot (mean decrease accuracy and mean decrease Gini).
This is a fundamental outcome of the random forest and it shows, for each variable, how important it is in classifying the data. The Mean Decrease Accuracy plot expresses how much accuracy the model losses by excluding each variable. The more the accuracy suffers, the more important the variable is for the successful classification. The variables are presented from descending importance. The mean decrease in Gini coefficient is a measure of how each variable contributes to the homogeneity of the nodes and leaves in the resulting random forest. The higher the value of mean decrease accuracy or mean decrease Gini score, the higher the importance of the variable in the model.
Table 2.