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  2. Confusion matrix - Wikipedia

    en.wikipedia.org/wiki/Confusion_matrix

    The template for any binary confusion matrix uses the four kinds of results discussed above (true positives, false negatives, false positives, and true negatives) along with the positive and negative classifications.

  3. Sensitivity and specificity - Wikipedia

    en.wikipedia.org/wiki/Sensitivity_and_specificity

    The true positive in this figure is 6, and false negatives of 0 (because all positive condition is correctly predicted as positive). Therefore, the sensitivity is 100% (from 6 / (6 + 0) ). This situation is also illustrated in the previous figure where the dotted line is at position A (the left-hand side is predicted as negative by the model ...

  4. False positives and false negatives - Wikipedia

    en.wikipedia.org/wiki/False_positives_and_false...

    The false positive rate (FPR) is the proportion of all negatives that still yield positive test outcomes, i.e., the conditional probability of a positive test result given an event that was not present. The false positive rate is equal to the significance level. The specificity of the test is equal to 1 minus the false positive rate.

  5. Precision and recall - Wikipedia

    en.wikipedia.org/wiki/Precision_and_recall

    In a classification task, the precision for a class is the number of true positives (i.e. the number of items correctly labelled as belonging to the positive class) divided by the total number of elements labelled as belonging to the positive class (i.e. the sum of true positives and false positives, which are items incorrectly labelled as belonging to the class).

  6. What Really Causes a False Positive COVID-19 Test? Experts ...

    www.aol.com/lifestyle/false-positive-covid-19...

    In the most basic sense, there are four possible outcomes for a COVID-19 test, whether it’s molecular PCR or rapid antigen: true positive, true negative, false positive, and false negative ...

  7. Positive and negative predictive values - Wikipedia

    en.wikipedia.org/wiki/Positive_and_negative...

    The positive predictive value (PPV), or precision, is defined as = + = where a "true positive" is the event that the test makes a positive prediction, and the subject has a positive result under the gold standard, and a "false positive" is the event that the test makes a positive prediction, and the subject has a negative result under the gold standard.

  8. Evaluation of binary classifiers - Wikipedia

    en.wikipedia.org/wiki/Evaluation_of_binary...

    The positive and negative prediction values would be 99%, so there can be high confidence in the result. However, if the prevalence is only 5%, so of the 2000 people only 100 are really sick, then the prediction values change significantly. The likely result is 99 true positives, 1 false negative, 1881 true negatives and 19 false positives.

  9. Likelihood ratios in diagnostic testing - Wikipedia

    en.wikipedia.org/wiki/Likelihood_ratios_in...

    Taking the medical example from above (20 true positives, 10 false negatives, and 2030 total patients), the positive pre-test probability is calculated as: Pretest probability = (20 + 10) / 2030 = 0.0148