信息论分类准确性的Python包itca：指导多类分类中模糊结果标签的数据驱动组合的标准。

IF 1.4 4区生物学 Q4 BIOCHEMICAL RESEARCH METHODS

Journal of Computational Biology Pub Date : 2023-11-01 Epub Date: 2023-11-06 DOI:10.1089/cmb.2023.0191

Chihao Zhang, Shihua Zhang, Jingyi Jessica Li

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引用次数: 0

摘要

itcaPython包提供了一个信息论标准，通过平衡预测准确性和分类分辨率之间的权衡，帮助从业者组合模糊的结果标签。本文提供了安装itcaPython包的说明，演示了如何评估该标准，并展示了它在实际场景中的应用，以指导模糊结果标签的组合。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A Python Package itca for Information-Theoretic Classification Accuracy: A Criterion That Guides Data-Driven Combination of Ambiguous Outcome Labels in Multiclass Classification.

The itca Python package offers an information-theoretic criterion to assist practitioners in combining ambiguous outcome labels by balancing the tradeoff between prediction accuracy and classification resolution. This article provides instructions for installing the itca Python package, demonstrates how to evaluate the criterion, and showcases its application in real-world scenarios for guiding the combination of ambiguous outcome labels.

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来源期刊

Journal of Computational Biology 生物-计算机：跨学科应用

CiteScore

3.60

自引率

5.90%

发文量

113

审稿时长

6-12 weeks

期刊介绍： Journal of Computational Biology is the leading peer-reviewed journal in computational biology and bioinformatics, publishing in-depth statistical, mathematical, and computational analysis of methods, as well as their practical impact. Available only online, this is an essential journal for scientists and students who want to keep abreast of developments in bioinformatics. Journal of Computational Biology coverage includes: -Genomics -Mathematical modeling and simulation -Distributed and parallel biological computing -Designing biological databases -Pattern matching and pattern detection -Linking disparate databases and data -New tools for computational biology -Relational and object-oriented database technology for bioinformatics -Biological expert system design and use -Reasoning by analogy, hypothesis formation, and testing by machine -Management of biological databases