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A comprehensive survey of error measures for evaluating binary decision making in data science.
Frank Emmert-Streib1, Salisou Moutari2, Matthias Dehmer3,4,5
1Predictive Society and Data Analytics Lab, Faculty of Information Technology and Communication Sciences Tampere University Tampere Finland.
Wiley Interdisciplinary Reviews. Data Mining and Knowledge Discovery
|October 29, 2019
Summary
Choosing the right error measures is crucial for evaluating binary decision-making methods across various data-driven fields. This survey provides a comprehensive overview of these essential metrics for accurate prediction assessment.
Area of Science:
- Data Mining
- Prediction
- Statistics
Background:
- Binary decision-making is vital across diverse disciplines like medicine, economics, and social sciences.
- Developing effective computational methods for binary decision-making requires careful evaluation.
- The selection of appropriate error measures is critical for assessing prediction method performance.
Purpose of the Study:
- To conduct a comprehensive survey of error measures for binary decision-making.
- To aid in the evaluation of prediction outcomes in data-driven fields.
- To simplify the complex task of selecting suitable error metrics.
Main Methods:
- Literature review and synthesis of existing error measures.
- Categorization of error measures based on their application in binary decision-making.
- Analysis of the importance and applicability of different error metrics.
Main Results:
- Identified and cataloged a wide range of error measures for binary classification.
- Highlighted the significance of selecting appropriate metrics for reliable evaluation.
- Provided a structured overview to guide researchers in choosing the best error measures.
Conclusions:
- A systematic approach to selecting error measures enhances the validity of binary decision-making models.
- This survey serves as a valuable resource for researchers and practitioners in data-driven fields.
- Standardizing the evaluation process through appropriate error metrics is essential for scientific rigor.
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