Decision tree (DT)
DT, as a binary method, can be used to classify data by calculating their characteristics. Decision nodes, branches and leaves are the three main components of DT. DT starts with a node and extends to many branches and child nodes, finally to leaves. The criterion used in our model is the Gini’s Diversity Index, which is a measure of node impurity. The standard CART algorithm, implemented using sciki-learn library in Python, was applied to build decision tree.The parameters set in the DT were: random_state = 0, max_features = 6, max_depth = 6, criterion = ’gini’. Other parameters were set as default in sklearn software module.