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Learning Sets of Rules

Other Performance Measures

  1. Relative frequency: Let $n$ be the number of examples the rule matches and $n_c$ be the number that it classifies correctly. \[\frac{n_c}{n}\]
  2. m-estimate of accuracy: Let $p$ be the prior probability that a random example will be correctly classified correctly, let $m$ be the weight. \[\frac{n_c + mp}{n+m}\]
  3. Entropy: Let $S$ be the set of examples that match the rule precondition, $c$ be the number of distinct values the target function make take on, and $p_i$ the proportion of examples for which the target function takes the $i$th value \[-\text{Entropy}(S) = \sum_{i=1}^c p_i\log_2 p_i\]

José M. Vidal .

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