# Confidence Score

A Confidence Score is a ordinal value associated to a prediction that is intended to distinguish between strong predictions over weak predictions.

**AKA:**Confidence Level, Prediction Likelihood Value, Prediction Confidence Estimate.**Context:**- It can be produced by a Confidence Estimation Function (produced by a Confidence Estimation).
- It can range from being a Coarse Confidence Score (such as a strong prediction score) to being a Fine-Grained Confidence Score.
- It can range from being a Classification Confidence Score to being a Rank Prediction Confidence Score being an Estimation Confidence Score.
- It can range from being an Overconfident Confidence Level to being an Underconfident Confidence Level.

**Example:***low likelihood*- 0.87 expected precision.
- In a Nearest Neighbor Algorithm-based Prediction the distance measure can be used as a confidence score, in that this value often results in the desired ranking.

**Counter-Example(s):****See:**Statistical Significance Level, Chunking Task, Frequent Event, Infrequent Event.

## References

### 2008

- (Braun et al., 2008) ⇒ P. Braun, et al. (2008). “An Experimentally Derived Confidence Score for Binary Protein-Protein Interactions.” In: Nat. Methods 6, 91–97.

### 2004

- (Culotta & McCallum, 2004) ⇒ Aron Culotta, and Andrew McCallum. (2004). “Confidence Estimation for Information Extraction.” In: Proceedings of HLT-NAACL (NAACL 2004).

### 1997

- (Susuki, 1997) ⇒ Einoshin Suzuki. (1997). “Autonomous Discovery of Reliable Exception Rules.” In: Proceedings of KDD Conference (KDD 1997)
- QUOTE: we propose a novel approach in which exception rules are discovered according to their
**confidence level**based on the normal approximations of the multinomial distributions. This approach can be called as autonomous, since an exception rule is discovered using neither users’ confidence evaluation nor domain knowledge.

- QUOTE: we propose a novel approach in which exception rules are discovered according to their