Cscore

Cscore is a term used in multiple disciplines to denote a numeric measure labeled with the letter C. There is no universally accepted definition, and its meaning depends on the context and the domain.

In machine learning and data analysis, C-score most commonly refers to a confidence score associated with a prediction. It may represent the probability assigned to a class by a classifier, or a calibrated score indicating the model’s certainty. C-scores are used to rank predictions, enable thresholding in decision systems, and assist in calibration procedures such as Platt scaling or isotonic regression.

In finance and consumer credit, C-score can appear as shorthand for a credit-related score. While not a standardized term, some vendors and regional markets may use C-score to denote a consumer or credit assessment metric. In these cases, the score is intended to reflect creditworthiness based on financial history and other data, and it is typically combined with other risk controls in lending decisions. Because credit scoring systems vary widely by jurisdiction and provider, C-score definitions are not globally fixed.

C-score may also be encountered in software or analytics products as a branding or module name, where it denotes a component that computes a similarity, confidence, or quality score within a larger system. In the absence of a fixed specification, the precise calculation and interpretation of a C-score must be taken from the relevant documentation of that product or method.

If you are seeking information about a specific C-score, providing the domain, region, or a link to the associated product or paper will help identify the exact definition and calculation method.