  The Machine Learning has various learning algorithms that are better in some
or the other aspect when compared with each other but a common error that all
algorithms will suffer from is training data with very high dimensional feature
set. This usually ends up algorithms into generalization error that deplete the
performance. This can be solved using an Ensemble Learning method known as
Stacking commonly termed as Stacked Generalization. In this paper we perform
binary classification using Stacked Generalization on high dimensional
Polycystic Ovary Syndrome dataset and prove the point that model becomes
generalized and metrics improve significantly. The various metrics are given in
this paper that also point out a subtle transgression found with Receiver
Operating Characteristic Curve that was proved to be incorrect.
