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How do we decide
which limit to use and how
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strong to make
the limit?
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If
we use the test data we get an unfair
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prediction
of the error rate we would get on
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new
test data.
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Suppose
we compared a set of models that
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gave
random results, the best one on a
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particular
dataset would do better than
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chance. But it wont do better than chance on
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another
test set.
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So use a
separate validation set to do model
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selection.
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