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.