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The training
data contains information about the
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regularities in
the mapping from input to output. But it
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also contains
noise
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The
target values may be unreliable.
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There
is sampling error. There will be accidental
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regularities
just because of the particular training
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cases
that were chosen.
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When we fit the
model, it cannot tell which regularities
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are real and
which are caused by sampling error.
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So
it fits both kinds of regularity.
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If
the model is very flexible it can model the sampling
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error
really well. This is a disaster.
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