Neutral Class
Adds an explicit uncertain class for genuinely ambiguous cases
Intent & Description
🎯 Intent
Handle cases where forcing a strict binary decision is misleading because the ground truth itself is genuinely ambiguous due to human rater disagreement or borderline cases.
📋 Context
Forcing a model to pick a side on genuinely ambiguous examples injects noise and distorts confidence calibration on the cases that actually are clear-cut. Human raters disagree substantially on some cases.
💡 Solution
Add an explicit neutral (or uncertain) class to what would otherwise be a binary problem. This honestly represents genuinely ambiguous cases, keeps the two original classes cleaner and more separable, and lets downstream systems treat neutral outputs differently (e.g., routing to human review).
Real-world Use Case
- Sentiment analysis with borderline or mixed sentiment
- Content moderation with subjective judgment calls
- Medical diagnosis with genuinely ambiguous cases
- Any task with inherent labeler disagreement or ambiguity
Source
📌 TL;DR
Add an explicit neutral/uncertain class for genuinely ambiguous cases instead of forcing binary decisions, improving model quality and enabling specialized handling
Advantages
- Honestly represents genuine ambiguity in data
- Keeps main classes cleaner and more separable
- Enables specialized handling of uncertain cases
- Improves calibration on clear-cut cases
Disadvantages
- Neutral class needs its own downstream handling logic
- Evaluation gets more complex with additional class
- Requires clear criteria for what qualifies as neutral
- May need relabeling effort to identify neutral cases