If a simple minimum-distance classifier is satisfactory, there is no reason to use anything more complicated. However, it frequently happens that such a classifier makes too many errors. There are several possible reasons for this:

  1. The features may be inadequate to distinguish the different classes
  2. The features may be highly correlated
  3. The decision boundary may have to be curved
  4. There may be distinct subclasses in the data
  5. The feature space may simply be too complex

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