Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
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发表时间：
2001-06
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通讯作者：
B. Zadrozny;C. Elkan
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作者：
B. Zadrozny;C. Elkan
在许多有监督的学习应用中，需要对类成员资格的准确估计，尤其是当必须对示例依赖成本做出成本敏感的决策，以呈现简单但成功的方法。决策树和天真的贝叶斯分类器。根据四种评估措施的详细实验比较。 。
Accurate, well-calibrated estimates of class membership probabilities are needed in many supervised learning applications, in particular when a cost-sensitive decision must be made about examples with example-dependent costs. This paper presents simple but successful methods for obtaining calibrated probability estimates from decision tree and naive Bayesian classifiers. Using the large and challenging KDD’98 contest dataset as a testbed, we report the results of a detailed experimental comparison of ten methods, according to four evaluation measures. We conclude that binning succeeds in significantly improving naive Bayesian probability estimates, while for improving decision tree probability estimates, we recommend smoothing by -estimation and a new variant of pruning that we call curtailment.