Figure 3
From: A Bayesian network approach to feature selection in mass spectrometry data
Error rate as features added. The cross-validated error rate decreases steadily as features are added according to their ability to increase the accuracy of a naïve Bayesian classifier. Two typical trials are shown. It is possible with a large number of variables to continue to choose ever-larger feature sets with ever-lower error rates, but these larger feature sets are caused by over-fitting and have unstable memberships.