3.4.2. Decision Tree
A decision tree is a decision support model that illustrates the consequences, chance, and event outcomes of certain decisions. Decision trees are used in computer science as a predictive model to make statistical conclusions about an item’s target value based on observations. In this tree structure, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. There are both classification trees where the response variable takes on a set of categorical values and regression trees where the response variable takes on a set of continuous values. The collective name for such trees is Classification and Regression Trees (CART), first introduced and developed by (L.Breiman, Friendman, Olshen and Stone 1984) in Classification and Regression Trees. A J48 decision tree was constructed for predicting the infection containment with the independent variables listed in figure 1. The batch size was set to 10 and a confidence factor was selected as 0.25. The minimum number of objects on the tree was set as 2. The accuracy of the tree was found to be 80.95%. The variables in the decision tree are percentage lockdown days, days since official lockdown, and death rate per million population. The decision tree is displayed in Figure 4.