Exracting additional knowledge and performance from configurational models that already have wide coverage

 

A decision tree algorithm, as available within EvalC3, can generate a classification tree / set of predictive models of the kind shown here.

Some of the models (each branch is a model) are very detailed (has lots of attributes) and have narrow coverage. Such as HasQuotas+NotPost Conflict Situation+High Level of Human Development+ Low Womens Status = Low levels of womens representation in Parliament, which covers two cases (Senegal, Tanzania). 

Others are quite simple, with only two or three attributes and can have much wider coverage. Such as HasQuotas+ IsPostConflict = High levels of womens representation in Parliament, which covers two six cases (Burundi, Ethiopia, Mozambique, Namibia, South Africa, Uganda)

These wide coverage models may have unexplored potential, in the form of unexploited information content. The raw (numerical) outcome data for the cases they cover can be examined and recalibrated i.e re-dichotomised into two new sub-groups representing relatively higher versus lower outcome values within that set only. A new configurational analysis could then focus on that set of cases to see if (a) any of the existing attrubutes could predict membership of the two groups, or (b) if any additional attributes, based on knowledge of these cases, could do so.The ability to predict finer grained performance differences would be a significant improvement.