Evaluation of Learning Algorithms Using ROC Curve

In this lesson, we will introduce the evaluation of the ROC curve for several parameter combinations, providing a quantitative measure of learning output performance. This analysis will help us understand not just the quality of the CPDs generated but also the predictive power and reliability of each algorithm.

First, we dive into the main parameters that we can use.

Bayes prior

The bayes_prior parameter in the Bayesian estimation methods for learning the parameters of a Bayesian network refers to the type of prior distribution that is applied during the estimation process.

BDeu as a parameter value:

• BDeu stands for "Bayesian Dirichlet equivalent uniform."

• It is a type of prior that treats all model structures (in terms of the CPDs) as equally likely a priori (hence the "uniform" part).

• BDeu is designed to be equivalent across different network structures that encode the same assertions of conditional independence (which relates to the "equivalent" part).

• The BDeu prior uses a parameter called the "equivalent sample size" which can be thought of as the weight given to the prior relative to the data. A larger equivalent sample size means the prior belief is stronger and ...