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Designing Graphical Causal Bayesian Networks in Python

Evaluating the Penalty Heuristic Using the ROC

Explore how to evaluate penalty heuristic models in Bayesian networks by analyzing ROC curves and AUC values. Learn to interpret model performance, identify overfitting risks, and tune thresholds for stronger classification accuracy.

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Building on our understanding of the relationships between random variables, dependencies, and the overall structure of the network, we focus on assessing the model's effectiveness.

Probability of passing through a node heuristic model

Let’s start with the evaluation of the ROC for an ad-hoc probabilistic logic approach:

Python 3.8
# Simulate data
n_rows = 10000
# Define node probabilities
node_probabilities = {
    'A': {True: 1},
    'B': {True: 0.375, False: 0.625},
    'C': {True: 0.625, False: 0.375},
    'D': {True: 0.6, False: 0.4},
    'E': {True: 0.4, False: 0.6},
    'F': {True: 0.25, False: 0.75},
    'G': {True: 1},
    'H': {True: 0.63, False: 0.37},
    'I': {True: 1, False: 0},
    'J': {True: 0.5, False: 0.5},
}
# Generate random boolean values for nodes based on the given probabilities
data = {
    node: np.random.choice(
        [True, False], size=n_rows, p=[prob[True], prob.get(False, 0)]
    )
    for node, prob in node_probabilities.items()
}

When the ROC curve is very close to the diagonal line (x=yx=y ...