Design a classifier that can identify

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Reference no: EM133186574

Question: A company manufactures personal protective equipment (PPE) for use in hospitals and personal use. The company has two manufacturing facilities (factories) from which these PPE are made before they are transported to a warehouse and packed for export. After a few consignments were delivered, customers complained about defective equipment.

The PPE are made from several components that could contribute to malfunctioning. It is desired to design a classifier that can identify which of the two factories produced a given defective PPE. Let the components of the PPE be available and measurable/testable.

They can be be modelled as d conditionally independent binary-valued features, x = (x1, . . . , xd ) t , where the components xi are either 0 or 1. The two factories are modelled as classes ω1 and ω2. Suppose the company has information about the reliability of the factories regarding each component. This information can be modelled as pi = Pr[xi = 1|ω1] qi = Pr[xi = 1|ω2], where pi and qi are respectively the probabilities of each factory making a non-defective component. Each feature thus gives us a yes/no answer about the pattern. Furthermore, if pi > qi , we expect the i th feature to give a "yes" answer more frequently when the state of nature is ω1 than when it is ω2.

Reference no: EM133186574

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