Computational Efficient Approximations of the Concordance Probability in a Big Data Setting

by   Robin Van Oirbeek, et al.

Performance measurement is an essential task once a statistical model is created. The Area Under the receiving operating characteristics Curve (AUC) is the most popular measure for evaluating the quality of a binary classifier. In this case, AUC is equal to the concordance probability, a frequently used measure to evaluate the discriminatory power of the model. Contrary to AUC, the concordance probability can also be extended to the situation with a continuous response variable. Due to the staggering size of data sets nowadays, determining this discriminatory measure requires a tremendous amount of costly computations and is hence immensely time consuming, certainly in case of a continuous response variable. Therefore, we propose two estimation methods that calculate the concordance probability in a fast and accurate way and that can be applied to both the discrete and continuous setting. Extensive simulation studies show the excellent performance and fast computing times of both estimators. Finally, experiments on two real-life data sets confirm the conclusions of the artificial simulations.


page 1

page 2

page 3

page 4


Concordance probability in a big data setting: application in non-life insurance

The concordance probability or C-index is a popular measure to capture t...

A Distributionally Robust Area Under Curve Maximization Model

Area under ROC curve (AUC) is a widely used performance measure for clas...

Model-free posterior inference on the area under the receiver operating characteristic curve

The area under the receiver operating characteristic curve (AUC) serves ...

An empirical evaluation of imbalanced data strategies from a practitioner's point of view

This research tested the following well known strategies to deal with bi...

AUC Maximization in the Era of Big Data and AI: A Survey

Area under the ROC curve, a.k.a. AUC, is a measure of choice for assessi...

The Area Under the ROC Curve as a Measure of Clustering Quality

The Area Under the the Receiver Operating Characteristics (ROC) Curve, r...

Connecting population-level AUC and latent scale-invariant R^2 via Semiparametric Gaussian Copula and rank correlations

Area Under the Curve (AUC) is arguably the most popular measure of class...

Please sign up or login with your details

Forgot password? Click here to reset