## Well-Trained PETs: Improving Probability Estimation Trees (2000)

Citations: | 37 - 6 self |

### BibTeX

@MISC{Provost00well-trainedpets:,

author = {Foster Provost and Pedro Domingos},

title = {Well-Trained PETs: Improving Probability Estimation Trees},

year = {2000}

}

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### OpenURL

### Abstract

Decision trees are one of the most effective and widely used classification methods. However, many applications require class probability estimates, and probability estimation trees (PETs) have the same attractive features as classification trees (e.g., comprehensibility, accuracy and efficiency in high dimensions and on large data sets). Unfortunately, decision trees have been found to provide poor probability estimates. Several techniques have been proposed to build more accurate PETs, but, to our knowledge, there has not been a systematic experimental analysis of which techniques actually improve the probability estimates, and by how much. In this paper we first discuss why the decision-tree representation is not intrinsically inadequate for probability estimation. Inaccurate probabilities are partially the result of decision-tree induction algorithms that focus on maximizing classification accuracy and minimizing tree size (for example via reduced-error pruning). Larger tree...