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Whereas TOC AUC varies between 0 and 1 — with an uninformative classifier yielding 0.5 — the alternative measures known as informedness, [citation needed] Certainty [12] and Gini coefficient (in the single parameterization or single system case) [citation needed] all have the advantage that 0 represents chance performance whilst 1 ...
For example, gentamicin is an antibiotic that can be nephrotoxic (kidney damaging) and ototoxic (hearing damaging); measurement of gentamicin through concentrations in a patient's plasma and calculation of the AUC is used to guide the dosage of this drug. [3] AUC becomes useful for knowing the average concentration over a time interval, AUC/t.
Phi measures how better (or worse) is a classification, with respect to the random classification, which is characterized by Phi = 0. According to the reference values suggested by Cohen, [8] one can take Phi = 0.35 as a minimum acceptable level of Phi for a classification. In the ROC space, Phi equal to a non null constant corresponds to the ...
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A high ROC AUC, such as 0.9 for example, might correspond to low values of precision and negative predictive value, such as 0.2 and 0.1 in the [0, 1] range. If one performed a binary classification, obtained an ROC AUC of 0.9 and decided to focus only on this metric, they might overoptimistically believe their binary test was excellent.
The exact definition of a penny stock varies, but typically they include stocks trading for less than $5 per share all the way down to even fractions of a penny.