ROC curves for regression

作者：

Highlights：

• We present a natural and powerful counterpart of ROC analysis for regression.

• The parallelism with classification evaluation suggests high expected applicability.

• RROC space and RROC curves are derived from asymmetric loss functions.

• The area over the RROC curve is shown to be equivalent to the error variance.

• Predictive model behaviour will be better understood by these links (MSE, Bias, Var).

摘要

Highlights•We present a natural and powerful counterpart of ROC analysis for regression.•The parallelism with classification evaluation suggests high expected applicability.•RROC space and RROC curves are derived from asymmetric loss functions.•The area over the RROC curve is shown to be equivalent to the error variance.•Predictive model behaviour will be better understood by these links (MSE, Bias, Var).

论文关键词：ROC Curves,Cost-sensitive regression,Operating condition,Asymmetric loss,Error variance,MSE decomposition

论文评审过程：Received 27 December 2012, Revised 1 May 2013, Accepted 11 June 2013, Available online 24 June 2013.

论文官网地址：https://doi.org/10.1016/j.patcog.2013.06.014