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  2. Sensitivity and specificity - Wikipedia

    en.wikipedia.org/wiki/Sensitivity_and_specificity

    In medicine and statistics, sensitivity and specificity mathematically describe the accuracy of a test that reports the presence or absence of a medical condition. If individuals who have the condition are considered "positive" and those who do not are considered "negative", then sensitivity is a measure of how well a test can identify true ...

  3. Precision and recall - Wikipedia

    en.wikipedia.org/wiki/Precision_and_recall

    Precision and recall are then defined as: [12] = + = + Recall in this context is also referred to as the true positive rate or sensitivity, and precision is also referred to as positive predictive value (PPV); other related measures used in classification include true negative rate and accuracy. [12]

  4. Evaluation of binary classifiers - Wikipedia

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

    The relationship between sensitivity and specificity, as well as the performance of the classifier, can be visualized and studied using the Receiver Operating Characteristic (ROC) curve. In theory, sensitivity and specificity are independent in the sense that it is possible to achieve 100% in both (such as in the red/blue ball example given above).

  5. F-score - Wikipedia

    en.wikipedia.org/wiki/F-score

    Precision and recall. In statistical analysis of binary classification and information retrieval systems, the F-score or F-measure is a measure of predictive performance. It is calculated from the precision and recall of the test, where the precision is the number of true positive results divided by the number of all samples predicted to be positive, including those not identified correctly ...

  6. 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.

  7. Binary classification - Wikipedia

    en.wikipedia.org/wiki/Binary_classification

    true positive rate (TPR) = (TP/(TP+FN)), aka sensitivity or recall. These are the proportion of the population with the condition for which the test is correct. with complement the false negative rate (FNR) = (FN/(TP+FN))

  8. Receiver operating characteristic - Wikipedia

    en.wikipedia.org/wiki/Receiver_operating...

    However, if this person took a look at the values of precision and negative predictive value, they might discover their values are low. The ROC AUC summarizes sensitivity and specificity, but does not inform regarding precision and negative predictive value. [17]

  9. Accuracy and precision - Wikipedia

    en.wikipedia.org/wiki/Accuracy_and_precision

    Accuracy is also used as a statistical measure of how well a binary classification test correctly identifies or excludes a condition. That is, the accuracy is the proportion of correct predictions (both true positives and true negatives) among the total number of cases examined. [10]

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