For example, in the credit card offer domain, a customer database could contain much incidental information such as number of children, length of time at job, house size, median income, make and model of car, average education level, and so on. Conceivably some of these could be relevant to whether the customer would accept the credit card offer, but probably most would be irrelevant. Such problems are said to be high-dimensional — they suffer from the so-called curse of dimensionality — and this poses problems for nearest neighbor methods. Much of the reason and effects are quite
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