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Binary classification
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==Converting continuous values to binary== {{anchor|artificial}} <!--Artificially binary value redirects here--> Binary classification may be a form of [[dichotomization]] in which a continuous function is transformed into a binary variable. Tests whose results are of continuous values, such as most [[blood values]], can artificially be made binary by defining a [[cutoff (reference value)|cutoff value]], with test results being designated as [[positive or negative test|positive or negative]] depending on whether the resultant value is higher or lower than the cutoff. However, such conversion causes a loss of information, as the resultant binary classification does not tell ''how much'' above or below the cutoff a value is. As a result, when converting a continuous value that is close to the cutoff to a binary one, the resultant [[Positive predictive value|positive]] or [[negative predictive value]] is generally higher than the [[predictive value]] given directly from the continuous value. In such cases, the designation of the test of being either positive or negative gives the appearance of an inappropriately high certainty, while the value is in fact in an interval of uncertainty. For example, with the urine concentration of [[Human chorionic gonadotropin|hCG]] as a continuous value, a urine [[pregnancy test]] that measured 52 mIU/ml of hCG may show as "positive" with 50 mIU/ml as cutoff, but is in fact in an interval of uncertainty, which may be apparent only by knowing the original continuous value. On the other hand, a test result very far from the cutoff generally has a resultant positive or negative predictive value that is lower than the predictive value given from the continuous value. For example, a urine hCG value of 200,000 mIU/ml confers a very high probability of pregnancy, but conversion to binary values results in that it shows just as "positive" as the one of 52 mIU/ml.
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