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A large (250 ml) glass of 12% ABV red wine has about three UK units of alcohol. A medium (175 ml) glass has about two UK units. A "medium" glass (175 ml (5.9 US fl oz) of 12% ABV wine contains around 2.1 units of alcohol. However, British pubs and restaurants often supply larger quantities (large glass ≈ 250 ml (8.5 US fl oz)), which contain ...
The threshold value to determine when a data point fits a model (t), and the number of inliers (data points fitted to the model within t) required to assert that the model fits well to data (d) are determined based on specific requirements of the application and the dataset, and possibly based on experimental evaluation.
The American Twelfth (10 2 ⁄ 3 US oz [315 mL], or 1 ⁄ 12 of a US gallon), American Commercial Pint (12.8 US fl oz [379 mL], or 1 ⁄ 10 of a US gallon) / British Reputed Pint (13 1 ⁄ 3 imp oz [379 mL], or 1 ⁄ 12 an Imperial gallon), and the Canadian "stubby" bottle (12 imp oz [341 mL]) may have been factors.
The liability-threshold model is a threshold model of categorical (usually binary) outcomes in which a large number of variables are summed to yield an overall 'liability' score; the observed outcome is determined by whether the latent score is smaller or larger than the threshold. The liability-threshold model is frequently employed in ...
A classification model (classifier or diagnosis [7]) is a mapping of instances between certain classes/groups.Because the classifier or diagnosis result can be an arbitrary real value (continuous output), the classifier boundary between classes must be determined by a threshold value (for instance, to determine whether a person has hypertension based on a blood pressure measure).
Volume fraction, which is widely used in chemistry (commonly denoted as v/v), is defined as the volume of a particular component divided by the sum of all components in the mixture when they are measured separately. For example, to make 100 mL of 50% alc/vol ethanol solution, water would be added to 50 mL of ethanol to make up exactly 100 mL.
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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).