Calibration (statistics) - in Classification

In Classification

Calibration in classification, see Classification (machine learning) and Statistical classification, is used to transform classifier scores into class membership probabilities. An overview of calibration methods for two-class and multi-class classification tasks is given by Gebel (2009).

The following univariate calibration methods exist for transforming classifier scores into class membership probabilities in the two-class case:

• Assignment value approach, see Garczarek (2002)
• Bayes approach, see Bennett (2002)
• Isotonic regression, see Zadrozny and Elkan (2002)
• Logistic regression, see Platt (1999)

The following multivariate calibration methods exist for transforming classifier scores into class membership probabilities in the case with classes count greater than two:

• Reduction to binary tasks and subsequent pairwise coupling, see Hastie and Tibshirani (1998)
• Dirichlet calibration, see Gebel (2009)