Eick: Introduction Machine Learning. Example: Credit scoring Differentiating between low-risk and high-risk customers from their income and savings. Classification. Discriminant: IF income > θ 1 AND savings > θ 2 THEN low-risk ELSE high-risk. Model. Why “Learn”?.
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Differentiating between low-risk and high-risk customers from their income and savingsClassification
Discriminant: IF income > θ1 AND savings > θ2
THEN low-risk ELSE high-risk
Definition := “KDD is the non-trivial process of
identifying valid, novel, potentially useful, and
ultimately understandable patterns in data” (Fayyad)
Alpydin & Ch. Eick: ML Topic1
Training examples of a person
AT&T Laboratories, Cambridge UK
y : price
y = g (x | θ)
g ( ) model,
y = wx+w0