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Recall bias is a type of measurement bias, and can be a methodological issue in research involving interviews or questionnaires. In this case, it could lead to misclassification of various types of exposure. [2]
The effect(s) of such misclassification can vary from an overestimation to an underestimation of the true value. [4] Statisticians have developed methods to adjust for this type of bias, which may assist somewhat in compensating for this problem when known and when it is quantifiable. [5]
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).
An F-score is a combination of the precision and the recall, providing a single score. There is a one-parameter family of statistics, with parameter β, which determines the relative weights of precision and recall. The traditional or balanced F-score is the harmonic mean of precision and recall:
Indexing and classification methods to assist with information retrieval have a long history dating back to the earliest libraries and collections however systematic evaluation of their effectiveness began in earnest in the 1950s with the rapid expansion in research production across military, government and education and the introduction of computerised catalogues.
This might be done in order to achieve "desireable", best performances for each class (potentially measured as precision and recall in each class). Finding the best multi-class classification performance or the best tradeoff between precision and recall is, however, inherently a multi-objective optimization problem.
President-elect Trump's adviser on government efficiency, Elon Musk, called for eliminating the Consumer Financial Protection Bureau.
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