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FAQ

Frequently asked questions

• What is Differential Privacy?

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  Differential Privacy ensures that the removal or addition of a single data record does not significantly impact the outcome of any analysis, protecting individual data.

• How does Machine Learning benefit from Bayesian Networks?

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  Bayesian Networks provide a probabilistic graphical model that enables reasoning under uncertainty, often improving interpretability and decision-making.

• What is the Exponential Mechanism in Differential Privacy?

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  The Exponential Mechanism selects outputs in a differentially private way by weighting them according to a quality function and a privacy budget parameter.

• What is MMPC?

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  MMPC (Max-Min Parents and Children) is a constraint-based algorithm for learning the structure of Bayesian networks using conditional independence tests.

• What is the significance of the F1-score in your analysis?

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  The F1-score measures the balance between precision and recall, offering a comprehensive evaluation metric for model performance in structure learning.