Affiliated Institutions
Related Publications
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation
Abstract Background To evaluate binary classifications and their confusion matrices, scientific researchers can employ several statistical rates, accordingly to the goal of the ...
Definitions, methods, and applications in interpretable machine learning
Significance The recent surge in interpretability research has led to confusion on numerous fronts. In particular, it is unclear what it means to be interpretable and how to sel...
Using Embeddings to Improve Named Entity Recognition Classification with Graphs
Richer information has potential to improve performance of NLP (Natural Language Processing) tasks such as Named Entity Recognition. A linear sequence of words can be enriched w...
Statistical and Machine Learning forecasting methods: Concerns and ways forward
Machine Learning (ML) methods have been proposed in the academic literature as alternatives to statistical ones for time series forecasting. Yet, scant evidence is available abo...
This Looks Like That: Deep Learning for Interpretable Image Recognition
When we are faced with challenging image classification tasks, we often explain our reasoning by dissecting the image, and pointing out prototypical aspects of one class or anot...
Publication Info
- Year
- 2025
- Type
- article
- Volume
- 12
- Issue
- 1
- Citations
- 0
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
0
OpenAlex
Cite This
Khaled Mahmud Sujon,
Rohayanti Hassan,
Kwonhue Choi
et al.
(2025).
Accuracy, precision, recall, f1-score, or MCC? empirical evidence from advanced statistics, ML, and XAI for evaluating business predictive models.
Journal Of Big Data
, 12
(1)
.
https://doi.org/10.1186/s40537-025-01313-4
Identifiers
- DOI
- 10.1186/s40537-025-01313-4