arXiv 1402.18922014-02stat.MLcs.IRcs.LG

最大化F1分数的保持分类器

Thresholding Classifiers to Maximize F1 Score

University of California, San Diego（加州大学圣地亚哥分校）

Zachary Chase Lipton, Charles Elkan, Balakrishnan Narayanaswamy

摘要：

本文提供了新的见解最大化F1分数的背景下，二进制分类，也在多标签分类的背景下。精确度和召回率的调和平均值，F1分数被广泛用于衡量一个分类器在一个类是罕见的情况下的成功。微观平均、宏观平均和每个实例平均F1分数用于多标签分类。对于任何产生实值输出的分类器，我们推导出最佳可实现的F1分数与实现此最佳值的决策阈值之间的关系。作为特殊情况，如果分类器输出是良好校准的条件概率，则最佳阈值是最佳F1分数的一半。作为另一种特殊情况，如果分类器完全没有信息，那么最佳行为是将所有示例分类为阳性。由于阳性实例的实际流行率通常较低，因此这种行为可以被认为是不期望的。作为一个案例研究，我们讨论的结果，这可能是令人惊讶的，应用此程序时，预测26,853标签的Medline文件。

英文摘要：

This paper provides new insight into maximizing F1 scores in the context of binary classification and also in the context of multilabel classification. The harmonic mean of precision and recall, F1 score is widely used to measure the success of a binary classifier when one class is rare. Micro average, macro average, and per instance average F1 scores are used in multilabel classification. For any classifier that produces a real-valued output, we derive the relationship between the best achievable F1 score and the decision-making threshold that achieves this optimum. As a special case, if the classifier outputs are well-calibrated conditional probabilities, then the optimal threshold is half the optimal F1 score. As another special case, if the classifier is completely uninformative, then the optimal behavior is to classify all examples as positive. Since the actual prevalence of positive examples typically is low, this behavior can be considered undesirable. As a case study, we discuss the results, which can be surprising, of applying this procedure when predicting 26,853 labels for Medline documents.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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