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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).
log(Diagnostic Odds Ratio) for varying sensitivity and specificity. In medical testing with binary classification, the diagnostic odds ratio (DOR) is a measure of the effectiveness of a diagnostic test. [1]
Pomalidomide, sold under the brand names Pomalyst and Imnovid, [7] [8] is an anti-cancer medication used for the treatment of multiple myeloma and AIDS-related Kaposi sarcoma. [ 7 ] Pomalidomide was approved for medical use in the United States in February 2013, [ 10 ] and in the European Union in August 2013. [ 8 ]
Confusion matrix; Pivot table, in spreadsheet software, cross-tabulates sampling data with counts (contingency table) and/or sums. TPL Tables is a tool for generating and printing crosstabs. The iterative proportional fitting procedure essentially manipulates contingency tables to match altered joint distributions or marginal sums.
The Fowlkes–Mallows index is an external evaluation method that is used to determine the similarity between two clusterings (clusters obtained after a clustering algorithm), and also a metric to measure confusion matrices. This measure of similarity could be either between two hierarchical clusterings or a clustering and a benchmark ...
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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.
Imagine a study evaluating a test that screens people for a disease. Each person taking the test either has or does not have the disease. The test outcome can be positive (classifying the person as having the disease) or negative (classifying the person as not having the disease).