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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).
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Confusion matrix; A confusion matrix can be used to quickly visualize the results of a classification (or clustering) algorithm. It shows how different a cluster is ...
Visualizing the bagging process. Sampling 4 patients from the original set with replacement and showing the out-of-bag sets. Only patients in the bootstrap sample would be used to train the model for that bag. This example shows how bagging could be used in the context of diagnosing disease.
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 ...
Confusion matrix. The relationship between sensitivity, specificity, and similar terms can be understood using the following table. Consider a group with P positive ...
In "Visualization Analysis and Design" Tamara Munzner writes "Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively." Munzner agues that visualization "is suitable when there is a need to augment human capabilities rather than replace people with computational ...
A matrix showing the predicted and actual classifications. A confusion matrix is of size l × l, where l is the number of different label values. The following confusion matrix is for l = 2: followed by the matrix. It does not, however, state that that is the standard convention, the matrix could be merely an example.