AUC: a misleading measure of the performance of predictive distribution models
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- Type
- article
- Published
- 2008-03-01
- Cited by
- 3,511
- References
- 55
- OpenAlex
- https://openalex.org/W2141014056
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:15206363
Keywords
Receiver operating characteristic, Measure (data warehouse), Statistics, Reliability (semiconductor), Goodness of fit
References
- Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment
- Modifying ROC Curves to Incorporate Predicted Probabilities
- Evaluating presence-absence models in ecology: the need to account for prevalence
- The continuing challenges of testing species distribution models
- The effects of species’ range sizes on the accuracy of distribution models: ecological phenomenon or statistical artefact?
- Comparing classifiers when the misallocation costs are uncertain
- The use of genetic algorithms and Bayesian classification to model species distributions
- Measuring the accuracy of diagnostic systems.
- On the statistical analysis of ROC curves.
- Receiver Operating Characteristic Methodology
- Sources, Sinks, and Population Regulation
- Species diversity in ecological communities: edited by Robert E. Ricklefs and Dolph Schluter University of Chicago Press, 1993. 105.00 hbk, 35.00 pbk (414 pages) ISBN 0 226 71822 0/0 226 71823 9
- Goodness of fit tests for the multiple logistic regression model
- Verification, Validation, and Confirmation of Numerical Models in the Earth Sciences
- A Coefficient of Agreement for Nominal Scales
- A Proposed Design and Analysis for Comparing Digital and Analog Mammography
- Selecting thresholds of occurrence in the prediction of species distributions
- Evaluating the predictive performance of habitat models developed using logistic regression
- An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data
- Robust Classification for Imprecise Environments
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- Mapping Reef Fish and the Seascape: Using Acoustics and Spatial Modeling to Guide Coastal Management
- Using Landscape and Bioclimatic Features to Predict the Distribution of Lions, Leopards and Spotted Hyaenas in Tanzania's Ruaha Landscape
- Predicting hospital aggression in secure psychiatric care
- The evolutionary history of Eugenia sect. Phyllocalyx (Myrtaceae) corroborates historically stable areas in the southern Atlantic forests.
- How sensitive are temperate tadpoles to climate change? The use of thermal physiology and niche model tools to assess vulnerability.
- Future effects of climate and land-use change on terrestrial vertebrate community diversity under different scenarios
- Differentiation of Predominantly Solid Enhancing Lipid-Poor Renal Cell Masses by Use of Contrast-Enhanced CT: Evaluating the Role of Texture in Tumor Subtyping.
- Leishmaniasis in Eurasia and Africa: geographical distribution of vector species and pathogens
- Social contextuality and conversational recommender systems
- Eco-Epidemiology of Eastern Equine Encephalitis Virus
- Automatic Detection and Intensity Estimation of Spontaneous Smiles
- Modelando ocorrência e abundância de espécies arbóreas no entorno de uma usina hidroelétrica no sul do Brasil
- Modeling the distribution of meadows in arid and semi-arid Patagonia, Argentina: assessing current distribution and predicting response to climate change
- Predictive distribution modeling for rare Himalayan medicinal plant Berberis aristata DC.
- Beyond simple means: integrating extreme events and biotic interactions in species distribution models: conservation implications for the northern bettong (Bettongia tropica) under climate change
- TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT
- Understanding fish-habitat associations using video observations and sonar imaging
- DNA-based Population Estimation, Harvest Vulnerability, and Home Range Dynamics of Black Bears in Western Maryland
- Detecting Command and Control Channels of a Botnet Using a N-packet-based Approach
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