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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]
In psychology, empathic accuracy is a measure of how accurately one person can infer the thoughts and feelings of another person.. The term was introduced in 1988, in conjunction with the term "empathic inference," by psychologists William Ickes and William Tooke. [1]
In psychology, interpersonal accuracy (IPA) refers to an individual's ability to make correct inferences about others' internal states, traits, or other personal attributes. [1] For example, a person who is able to correctly recognize emotions, motivation, or thoughts in others demonstrates interpersonal accuracy.
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).
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.
False precision (also called overprecision, fake precision, misplaced precision and spurious precision) occurs when numerical data are presented in a manner that implies better precision than is justified; since precision is a limit to accuracy (in the ISO definition of accuracy), this often leads to overconfidence in the accuracy, named precision bias.
In addition to accuracy of individual statements, it is an objective that articles provide overall accurate coverage of the topic, albeit the latter is less clear-cut. This includes balanced coverage, with inclusion weighted by degree of significance, informativeness on the topic, and directness of relevance to the topic.
The accuracy of the result justifies an attempt to derive a multiplicative function for that average, for example, y ∼ x + 2.67. {\displaystyle y\sim x+2.67.} One should be careful though, because the multiplicative function will be defined for the whole interval.