Rule accuracy.
Consider a training set that contains 100 positive examples and 400 negative
examples. For each of the following candidate rules,
R1: A ?? + (covers 4 positive and 1 negative examples),
R2: B ?? + (covers 30 positive and 10 negative examples),
R3: C ?? + (covers 100 positive and 90 negative examples),
determine which is the best and worst candidate rule according to:
The accuracies of the rules are 80% (for R1), 75% (for R2), and 52.6%
(for R3), respectively. Therefore R1 is the best candidate and R3 is the
worst candidate according to rule accuracy.
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