Evaluating Accuracy from the ROC Curve

In this lesson, we will examine the application of ROC curves to assess the accuracy of the complex BN model we created on the last mini-project. ROC curves offer a valuable way to compare the impact of individual features on prediction accuracy, shedding light on our model's performance.

Remember that the ROC curve, a widely used graphical representation in Bayesian network analysis, plots the true positive rate (or recall) against the false positive rate at different classification thresholds. This curve demonstrates the trade-off between sensitivity and specificity, enabling an effective model performance evaluation.

The area under the curve (AUC) quantifies the two-dimensional area beneath the ROC curve, ranging from the origin (0,0) to the point (1,1). The AUC serves as an indicator of the model's overall classification performance. A higher AUC value suggests better performance in distinguishing between positive and negative outcomes.

Plot the final model

Let’s start with the Bayesian network of our last mini-project: