Designing Graphical Causal Bayesian Networks in Python/

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Evaluating the Penalty Heuristic Using the ROC

Evaluating the Penalty Heuristic Using the ROC

Master the interpretation of ROC curves to effectively evaluate classifier performance, identify trade-offs, and improve model 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:

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# 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 ...