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Learn how to measure the performance of data retrieval or classification using precision and recall, and how to plot them on a precision-recall curve. See examples, formulas, and related metrics such as F-measure and Matthews correlation coefficient.
Learn how to calculate and interpret the PPV and NPV of a diagnostic test or other statistical measure. See the definitions, formulas, diagrams, and examples of these metrics and their relationship with sensitivity, specificity, and prevalence.
Learn how sensitivity and specificity measure the accuracy of a test that reports the presence or absence of a condition. Sensitivity is the probability of a positive test result, conditioned on the individual truly being positive, while specificity is the probability of a negative test result, conditioned on the individual truly being negative.
A receiver operating characteristic curve (ROC curve) is a graphical plot that illustrates the performance of a binary classifier model at varying threshold values. The ROC curve is the plot of the true positive rate (TPR) against the false positive rate (FPR) at each threshold setting.
Learn how to calculate and apply positive and negative likelihood ratios (LR+ and LR-) to assess the value of a diagnostic test. See examples, tables, and formulas for estimating pre-test and post-test probabilities of disease.
Rivalta test is a simple method to differentiate transudates and exudates in body fluids. It involves adding acetic acid to the fluid and observing the reaction. A positive test indicates an exudate, which may be due to inflammation or infection.
Learn how to estimate the probability of a condition before and after a diagnostic test, using different methods and formulas. Compare the advantages and disadvantages of various approaches, such as predictive values, likelihood ratio, relative risk, and diagnostic criteria.
F-score or F-measure is a metric that combines precision and recall of a binary classification or information retrieval system. It can be calculated using different weights and is related to other measures such as accuracy, specificity, and sensitivity.