Terms validaty
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Terms validaty

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    Terms validaty Terms validaty Presentation Transcript

    • Terms-validaty Eman youssif
    • definition Sensitivity and specificity are statistical measures of the performance of a binary classification test, also known in statistics as classification function.
    • definition Sensitivity (also called the true positive rate, or the recall rate in some fields) measures the proportion of actual positives which are correctly identified as such (e.g. the percentage of sick people who are correctly identified as having the condition).
    • Imagine a study evaluating a new test that screens people for a disease. Each person taking the test either has or does not have the disease. The test outcome can be positive (predicting that the person has the disease) or negative (predicting that the person does not have the disease). The test results for each subject may or may not match the subject's actual status. In that setting: True positive: Sick people correctly diagnosed as sick False positive: Healthy people incorrectly identified as sick True negative: Healthy people correctly identified as healthy False negative: Sick people incorrectly identified as healthy
    • In general, Positive = identified and negative = rejected. Therefore: True positive = correctly identified False positive = incorrectly identified True negative = correctly rejected False negative = incorrectly rejected
    • Sensitivity Sensitivity relates to the test's ability to identify a condition correctly. Consider the example of a medical test used to identify a disease. Sensitivity of the test is the proportion of people known to have the disease, who test positive for it.
    • Precision and recall In pattern recognition and information retrieval with binary classification, precision (also called positive predictive value) is the fraction of retrieved instances that are relevant, while recall (also known as sensitivity) is the fraction of relevant instances that are retrieved
    • there is an inverse relationship between precision and recall, where it is possible to increase one at the cost of reducing the other.