تقرير
Trinary Tools for Continuously Valued Binary Classifiers
| العنوان: | Trinary Tools for Continuously Valued Binary Classifiers |
|---|---|
| المؤلفون: | Gleicher, Michael, Yu, Xinyi, Chen, Yuheng |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction |
| الوصف: | Classification methods for binary (yes/no) tasks often produce a continuously valued score. Machine learning practitioners must perform model selection, calibration, discretization, performance assessment, tuning, and fairness assessment. Such tasks involve examining classifier results, typically using summary statistics and manual examination of details. In this paper, we provide an interactive visualization approach to support such continuously-valued classifier examination tasks. Our approach addresses the three phases of these tasks: calibration, operating point selection, and examination. We enhance standard views and introduce task-specific views so that they can be integrated into a multi-view coordination (MVC) system. We build on an existing comparison-based approach, extending it to continuous classifiers by treating the continuous values as trinary (positive, unsure, negative) even if the classifier will not ultimately use the 3-way classification. We provide use cases that demonstrate how our approach enables machine learning practitioners to accomplish key tasks. Comment: Author's version of journal paper accepted to appear |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1016/j.visinf.2022.04.002 |
| الوصول الحر: | http://arxiv.org/abs/2204.08136Test |
| رقم الانضمام: | edsarx.2204.08136 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1016/j.visinf.2022.04.002 |
|---|