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In predictive analytics, a table of confusion (sometimes also called a confusion matrix) is a table with two rows and two columns that reports the number of true positives, false negatives, false positives, and true negatives. This allows more detailed analysis than simply observing the proportion of correct classifications (accuracy).
The Confusion Assessment Method (CAM) is a diagnostic tool developed to allow physicians and nurses to identify delirium in the healthcare setting. [1] It was designed to be brief (less than 5 minutes to perform) and based on criteria from the third edition-revision of the Diagnostic and Statistical Manual of Mental Disorders (DSM-III-R) .
These can be arranged into a 2×2 contingency table (confusion matrix), conventionally with the test result on the vertical axis and the actual condition on the horizontal axis. These numbers can then be totaled, yielding both a grand total and marginal totals. Totaling the entire table, the number of true positives, false negatives, true ...
(Original text) : A lens/ray diagram to facilitate calculating a circle of confusion diameter c from focal length f, focused subject distance S1 and out-of-focus subject distance S2. File usage The following page uses this file:
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).
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In photographic optics, the Zeiss formula is a supposed formula for computing a circle of confusion (CoC) criterion for depth of field (DoF) calculations. The formula is c = d / 1730 {\displaystyle c=d/1730} , where d {\displaystyle d} is the diagonal measure of a camera format, film, sensor, or print, and c {\displaystyle c} the maximum ...
The definition of matrix multiplication is that if C = AB for an n × m matrix A and an m × p matrix B, then C is an n × p matrix with entries = =. From this, a simple algorithm can be constructed which loops over the indices i from 1 through n and j from 1 through p, computing the above using a nested loop: