Modifying ROC Curves to Incorporate Predicted Probabilities

No abstract is available for this paper.

• Publication year

  2005

• Venue

  Unknown venue

• Publication date

  Unknown publication date

• Fields of study

  Mathematics, Computer Science

• Identifiers

  No identifiers available.

• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Confidence Bands for ROC Curves: Methods and an Empirical Study

  2004cited by this paper
• Robust Classification for Imprecise Environments

  2000cited by this paper
• Better decisions through science.

  2000cited by this paper
• Comparing classifiers when the misallocation costs are uncertain

  1999cited by this paper
• Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions

  1997cited by this paper
• Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine.

  1993cited by this paper
• ROC curves, test accuracy, and the description of diagnostic tests.

  1991cited by this paper
• Comparing the Areas under More Than Two Independent ROC Curves

  1987cited by this paper
• The meaning and use of the area under a receiver operating characteristic (ROC) curve.

  1982cited by this paper

• Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance.

  2025cites this paper
• Evaluating The Predictive Reliability of Neural Networks in Psychological Research With Random Datasets

  2024cites this paper
• Is the Area Under Curve Appropriate for Evaluating the Fit of Psychometric Models?

  2022cites this paper
• G-AUC: An improved metric for classification model selection

  2022cites this paper
• Enhancing Graph Neural Networks via auxiliary training for semi-supervised node classification

  2021cites this paper
• Explainable models for forecasting the emergence of political instability

  2021cites this paper
• A Novel Performance Measure for Machine Learning Classification

  2021cites this paper
• Deep Learning for Virtual Screening: Five Reasons to Use ROC Cost Functions

  2020influential citation
• W-Index: An Index for Evaluating Link Prediction considering Only the Role of Wins

  2020cites this paper
• Statistical Methodology to Establish a Benchmark for Evaluating Antimicrobial Resistance Genes through Real Time PCR assay

  2020cites this paper
• Efecto del cambio climático en la distribución de especies clave en la vegetación de duna costera en la península de Yucatán, México

  2020cites this paper
• Probabilistic Extension of Precision, Recall, and F1 Score for More Thorough Evaluation of Classification Models

  2020cites this paper
• 3D ROC Histogram: A New ROC Analysis Tool Incorporating Information on Instances

  2019cites this paper
• CLIMATE CHANGE AND CHAGAS DISEASE IN THE AMERICAS: A QUALITATIVE SYSTEMATIC REVIEW

  2019cites this paper
• Maximization of AUC and Buffered AUC in binary classification

  2018influential citation
• A new metric to assess the predictive accuracy of multinomial land cover models

  2017cites this paper
• A Survey of Predictive Modeling on Imbalanced Domains

  2016cites this paper
• Bipartite Ranking: a Risk-Theoretic Perspective

  2016cites this paper
• Recent methods from statistics and machine learning for credit scoring

  2014cites this paper
• An Empirical Evaluation of Ranking Measures With Respect to Robustness to Noise

  2014cites this paper
• ROC analysis of classifiers in machine learning: A survey

  2013cites this paper
• Probabilistic Confusion Entropy for Evaluating Classifiers

  2013cites this paper
• ROC-ConCert: ROC-Based Measurement of Consistency and Certainty

  2012cites this paper
• A graphical analysis of cost-sensitive regression problems

  2012cites this paper
• A Unified View of Performance Metrics: Translating Threshold Choice into Expected Classification Loss C` Esar Ferri

  2012cites this paper
• A Unified View of Performance Metrics: Translating Threshold Choice into Expected Classification Loss C` Esar Ferri

  2012cites this paper
• A unified view of performance metrics: translating threshold choice into expected classification loss

  2012cites this paper
• Soft (Gaussian CDE) regression models and loss functions

  2012cites this paper
• Soft (Gaussian CDE) regression models and loss functions

  2012cites this paper
• Improved Credit Scoring with Multilevel Statistical Modelling

  2011cites this paper
• Threshold Choice Methods: the Missing Link

  2011cites this paper
• Integrated discrimination improvement and probability‐sensitive AUC variants

  2010cites this paper
• An experimental comparison of performance measures for classification

  2009cites this paper
• Template Matching Techniques in Computer Vision

  2009cites this paper
• ON EVALUATION

  2009cites this paper
• Template Matching Techniques in Computer Vision: Theory and Practice

  2009cites this paper
• Multi-class AUC metrics and weighted alternatives

  2008cites this paper
• A critical analysis of variants of the AUC

  2008cites this paper
• AUC: a misleading measure of the performance of predictive distribution models

  2008cites this paper
• An Improved Model Selection Heuristic for AUC

  2007cites this paper
• SELF : Software Engineering and Lightweight Formalisms TIN 2004-07943-C 04

  2007cites this paper
• A Better Measure than Accuracy in Classification Learning System

  2006cites this paper
• Reinventing Machine Learning with ROC Analysis

  2006cites this paper
• A scored AUC Metric for Classifier Evaluation and Selection

  2005cites this paper
• Scored AUC Metrics for Classifier Evaluation and Selection

  2005cites this paper
• Using the mimetic technique to increase AUC . Some very preliminary ideas

  2004cites this paper