Results 61 to 70 of about 2,524,964 (264)

ROCS: receiver operating characteristic surface for class-skewed high-throughput data. [PDF]

PLoS ONE, 2012
The receiver operating characteristic (ROC) curve is an important tool to gauge the performance of classifiers. In certain situations of high-throughput data analysis, the data is heavily class-skewed, i.e.
Tianwei Yu
doaj   +1 more source

Review on ROC Curves in the Presence of Covariates

Revstat Statistical Journal, 2013
REVSTAT-Statistical Journal, Vol. 12 No.
Pardo Fernández, Juan Carlos, Rodriguez Alvarez, Maria Jose, Keilegom, Ingrid van   +2 more
openaire   +5 more sources

Interrelationships Among Sensitivity, Precision, Accuracy, Specificity and Predictive Values in Bioassays, Represented as Combined ROC Curves with Integrated Cutoff Distribution Curves and Novel Index Values

Diagnostics
Background/Objectives: This work introduces accuracy- and precision-ROC curves in addition to SS– and PV–ROC curves and shows a novel means of profiling biomarker characteristics for validation of optimal cutoffs in clinical diagnostics and decision ...
Peter Oehr
doaj   +1 more source

Exploring Features for Predicting Policy Citations

, 2017
In this study we performed an initial investigation and evaluation of altmetrics and their relationship with public policy citation of research papers. We examined methods for using altmetrics and other data to predict whether a research paper is cited ...
Alhoori, Hamed, Bailey, Christian, Kale, Bharat, Papka, Micheal E., Siravuri, Harish Varma, Walker, Jamieson   +5 more
core   +1 more source

Estimation and Comparison of Receiver Operating Characteristic Curves [PDF]

, 2008
The receiver operating characteristic (ROC) curve displays the capacity of a marker or diagnostic test to discriminate between two groups of subjects, cases versus controls.
Janes, Holly, Longton, Gary M., Pepe, Margaret   +2 more
core   +1 more source

ROC curves and the X2 test [PDF]

Pattern Recognition Letters, 1996
In this paper we review the Receiver Operating Characteristic (ROC) curve, and the X^2 test statistic, in relation to the analysis of a confusion matrix. We then show how these two methods are related, and propose an extension to the ROC curve so that it shows contours of X^2 values.
openaire   +3 more sources

The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification

BioData Mining, 2023
Binary classification is a common task for which machine learning and computational statistics are used, and the area under the receiver operating characteristic curve (ROC AUC) has become the common standard metric to evaluate binary classifications in ...
Davide Chicco, Giuseppe Jurman
doaj   +1 more source

cutpointr: Improved Estimation and Validation of Optimal Cutpoints in R

Journal of Statistical Software, 2021
"Optimal cutpoints" for binary classification tasks are often established by testing which cutpoint yields the best discrimination, for example the Youden index, in a specific sample.
Christian Thiele, Gerrit Hirschfeld
doaj   +1 more source

Accommodating Covariates in ROC Analysis [PDF]

, 2008
Classification accuracy is the ability of a marker or diagnostic test to discriminate between two groups of individuals, cases and controls, and is commonly summarized using the receiver operating characteristic (ROC) curve.
Janes, Holly, Longton, Gary M., Pepe, Margaret   +2 more
core   +1 more source

ROC curves for multivariate markers

, 2021
Binary classification is a very common problem whose objective is to correctly determine whether or not a subject has one characteristic of interest. On the basis of a gold standard, the objective is to discriminate between two populations (positive and negative, depending on having or not the characteristic of interest, respectively) by means of a ...
openaire   +3 more sources