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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 25·5 min read
Counterfactual Transition Graphs: Evaluating Cross-Class Transition Quality
TL;DR
Counterfactual Transition Graphs (CTGs) were developed to evaluate the quality of transitions between classes in time-series classifiers. The findings reveal that counterfactual reachability does not align with classifier accuracy, indicating a complex relationship between class boundaries and prediction confidence.
✦ Why It Matters
Evaluate your classifier's transition quality using CTGs to better understand its decision-making process.
Key Takeaways
- Use CTGs to analyze class transition quality in your models.
- Investigate the relationship between counterfactual reachability and classifier accuracy.
- Compare different counterfactual explainers to enhance interpretability.
- Be aware that confident predictions may not indicate easy transitions between classes.
How It Works
CTGs are constructed by treating each class as a node and measuring the reliability of transitions between classes as edge weights. This approach allows for a comprehensive view of how a classifier connects its classes, revealing insights that individual example evaluations cannot provide.