The authors propose an inverse conformal prediction method for estimating misclassification risk in multi-class classifiers and show empirically that it is competitive with calibration techniques while being conservative.
Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
method 1
citation-polarity summary
fields
cs.LG 1years
2024 1verdicts
CONDITIONAL 1roles
method 1polarities
use method 1representative citing papers
citing papers explorer
-
An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms
The authors propose an inverse conformal prediction method for estimating misclassification risk in multi-class classifiers and show empirically that it is competitive with calibration techniques while being conservative.