What is Specificity

1.

The proportion of negative diagnosis that are correctly identified. Learn more in: A Thorough Presentation of Autism Diagnostic Observation Schedule (ADOS-2)

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If the test diagnoses the patient without disease as not having disease, then it is referred to as specificity. Learn more in: Characterization of Elevated Tumor Markers in Diagnosis of HCC Using Data Mining Methods

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the probability that a diagnostic test can correctly specify a non-case. Learn more in: ROC Analysis in Business Decision-makings

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The probability that the test result is negative for those participants with no clinical condition. A test that has high specificity will have few false positives results, but increases the false negative rate. Specificity is also known as True Negative Rate (TNR). Sensitivity and specificity are inversely related. Learn more in: The Clinical Utility of Psychometric Tests: A Real-Data Approach From a Study Including Children With ADHD

5.

The structure and content of vocational education (all levels and at different levels)that is adequate to the actual needs of social production, expressed in professional standards. Acceptability and Realistic: for studying the conditions (in any place, at any time and through all my life) to obtain the Knowledge, Skills and Abilities to develop the necessary competences and qualifications of specialists, caused by the requirements of PS and specific for a certain period of time. Learn more in: The Globalization of Education and Control Techniques of the E-Learning Systems in Russian Smart Universities for Increase of Quality and Competitiveness

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Success rate measure in a classification problem. If there are two classes (namely, positive and negative), specificity measures the ratio of negatives that are correctly classified by the model. Learn more in: An AI Walk from Pharmacokinetics to Marketing

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Estimation accuracy for non-bankrupt businesses. Learn more in: Risk of Business Bankruptcy: The Application of DEA Method – Case Study of Slovak Businesses

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The probability that a diagnostic test can correctly specify a non-case. Learn more in: Using Receiver Operating Characteristic (ROC) Analysis to Evaluate Information-Based Decision-Making

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An error measure that identifies how the alignments help us represent the image set compactly. Learn more in: Groupwise Non-Rigid Image Alignment Using Few Parameters: Registration of Facial and Medical Images

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The percentage of patients who do not have a specific diagnosis or desired outcome that are correctly identified, which is the number of true negatives divided by the sum of the true negatives and false positives. Learn more in: Artificial Neural Networks in Medicine: Recent Advances

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Specificity is a percentage of correctly classified normal samples, and it is given by the ratio of true negative to the addition of true negative and false positive samples. Learn more in: Text Separation From Document Images: A Deep Learning Approach

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Specificity is percentage of correctly classified normal samples and it is given by the ratio of true negative to the addition of true negative and false positive samples. Learn more in: Algorithms for Detection and Classification of Abnormality in Mammograms: An Overview

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The probability that a diagnostic test can correctly specify a non-case. Learn more in: Using Receiver Operating Characteristic (ROC) Analysis to Evaluate Information -Based Decision-Making

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Organizational knowledge that loses some of its value when removed from its original application and firm. Learn more in: Protecting Knowledge Assets

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The performance metric for binary classifiers indicating the percentage of true negative cases correctly labelled as negative by the system. Learn more in: Best Practices in Dropout Prediction: Experience-Based Recommendations for Institutional Implementation

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It is the ratio of true negatives to the sum of the true negatives and false positives. Learn more in: Machine Learning in Python: Diabetes Prediction Using Machine Learning

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Measure of the ability of a clinician or decision support system to correctly identify those who do not have the disease. Learn more in: Towards an Effective Imaging-Based Decision Support System for Skin Cancer

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One end of a dimension defined by generality in evaluating paradigms, theories, and models (se Generality). Learn more in: Of Paradigms, Theories, and Models: A Conceptual Hierarchical Structure for Communication Science and Technoself

19.

The proportion of negative diagnosis that are correctly identified. Learn more in: Approaches of Early Detection of Autism Spectrum Disorders: A Brief Review

20.

True negative rate, the probability of not detecting a hazard when it is absent. Learn more in: Surveillance Design After Initial Detection

21.

The proportion of true negative cases that are correctly identified. Learn more in: Systematic Literature Review: XAI and Clinical Decision Support

22.

It is the parameter used to assess a model's capability to predict the proportion of actual negative cases that were predicted by model as negative (or true negative). Learn more in: Brain Tumor Segmentation Using Deep Learning Technique: 2D U-Net Model Variant for Tumor Segmentation