An Empirical Comparison of Probability Estimation Techniques for Probabilistic Rules
Rule learning is known for its descriptive and therefore comprehensible classification models which also yield good class predictions. However, in some application areas, we also need good class probability estimates. For different classification models, such as decision trees, a variety of techniques for obtaining good probability estimates have been proposed and evaluated. However, so far, there has been no systematic empirical study of how these techniques can be adapted to probabilistic rules and how these methods affect the probability-based rankings. In this paper we apply several basic methods for the estimation of class membership probabilities to classification rules. We also study the effect of a shrinkage technique for merging the probability estimates of rules with those of their generalizations.
KeywordsProbability Estimation Rule Learning Class Probability Default Rule Rule Pruning
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