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BAYESIA

BAYESIA. Learning from a data base. BAYESIA. BAYESIA. BAYESIA. BAYESIA. BAYESIA. BAYESIA. BAYESIA. BAYESIA. Generating data from a Bayesian network. BAYESIA. BAYESIA. Generate two data bases (50 and 500 instances and different percentage of missing data)

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BAYESIA

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  1. BAYESIA Learning from a data base

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  9. BAYESIA Generating data from a Bayesian network

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  12. Generate two data bases (50 and 500 instances • and different percentage of missing data) • from the “Asia.xbl” Bayesian network • 2. Apply the following learning algoithms: • “EQ”, “SopLEQ”, “Tabo” and “TaboOrder” • to both data bases • 3. Compare the induced Bayesian networks with the • “Asia.xbl” • 4. Obtain information in Internet about the learning • algorithms BAYESIA

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  22. Exercise • Generate 3 files (100, 200 and 400 cases) from the • “Asia.xbl” Bayesian network • 2. Choose variable “Cancer” as the class (target) variable • 3. Induce the following classifiers: • Naive Bayes • Augmented naive Bayes • Markov blanket • 4. Compare the accuracies of the different models in the • 3 files BAYESIA

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