Tunable structure priors for Bayesian rule learning for knowledge integrated biomarker discovery

Figure 4 Bayesian rule learning generated rule model with λ=10 (highest average area under the receiver operator characteristic curve) on the simulated dataset. Each rule has its posterior probability, the number of true positives (TP), false positives (FP), total number of examples that match the rules consequent target value (Pos), and total number that do not match the right hand side of the rule (Neg). The TP measures examples that correctly match the rules left and right hand sides, while FP measures examples that correctly match the rules condition or left-hand-side, but have a different consequent or right-hand-side.