Explaining bayesian belief networks

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Bayesian Belief Networks

A quality control manager has used algorithm C4.5 to come up with rules that classify items based on several input factors. The output has two classes -- Accept and Reject.
Test results with the rule set indicate that 5% of the good items are classified as Reject and 2% of the bad items classified as Accept.

Historical data suggests that one percent of the items are bad. Based on this information, what is the conditional probability that:

(i) An item classified as Reject is actually good?

(ii) An item classified as Accept is actually bad?

Reference no: EM1352520

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