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L01 - CS165B Well Posed Learning Problems A Data Mining...

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CS165B Well Posed Learning Problems
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CS165B: Learning Problems 2 A Data Mining Example ± Given: 9714 patient records, each describing a pregnancy and birth Each patient record contains 215 features ± Learn to predict: Classes of future patients at high risk for Emergency Cesarean Section time=1 Patient103 Age: 23 FirstPregnancy: no Anemia: no Diabetes: no PreviousPrematureBirth: no Ultrasound: ? Elective C-Section: ? Emergency C-Section: ? . . . time=2 Patient103 Age: 23 FirstPregnancy: no Anemia: no Diabetes: yes PreviousPrematureBirth: no Ultrasound: abnormal Elective C-Section: no Emergency C-Section: ? . . . time= n Patient103 Age: 23 FirstPregnancy: no Anemia: no Diabetes: yes PreviousPrematureBirth: no Ultrasound: abnormal Elective C-Section: no Emergency C-Section: YES . . .
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CS165B: Learning Problems 3 Examples of Rules Learned ± One of 18 learned rules: If No previous vaginal delivery , and Abnormal 2nd Trimester Ultrasound , and Malpresentation at admission Then Probability of Emergency C-Section is 0.6 ± Over training data: 26/41 = .63 , ± Over test data: 12/20 = .60
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CS165B: Learning Problems 4 Credit Risk Analysis Rules learned from synthesized data: ± If Other-Delinquent-Accounts > 2 , and Number-Delinquent-Billing-Cycles > 1 then Profitable-Customer? = No [Deny Credit Card application] ± If Other-Delinquent-Accounts = 0 , and ( Income > $30k) OR ( Years-of-Credit > 3) then Profitable-Customer? = Yes [Accept Credit Card application] time=1 Customer103 Years of credit: 9 Loan balance: $2,400 Income: $52k Own House: Yes Other delinquent accts: 2 Max billing cycles late: 3 Profitable customer?: ?
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