Search results
Results From The WOW.Com Content Network
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).
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 ...
Different from the above approaches, if an imbalance scaling is applied directly by weighting the confusion matrix elements, the standard metrics definitions still apply even in the case of imbalanced datasets. [19] The weighting procedure relates the confusion matrix elements to the support set of each considered class.
In this confusion matrix, of the 8 cat pictures, the system judged that 2 were dogs, and of the 4 dog pictures, it predicted that 1 was a cat. All correct predictions are located in the diagonal of the table (highlighted in bold), so it is easy to visually inspect the table for prediction errors, as they will be represented by values outside ...
Confusion matrix. The relationship between sensitivity, specificity, and similar terms can be understood using the following table. Consider a group with P positive ...
Each prediction result or instance of a confusion matrix represents one point in the ROC space. The best possible prediction method would yield a point in the upper left corner or coordinate (0,1) of the ROC space, representing 100% sensitivity (no false negatives) and 100% specificity (no false positives).
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.
There are 20 dots on the left side of the line (true side) while only 8 of those 20 were actually true. In a similar situation for the right side of the line (false side) where there are 16 dots on the right side and 4 of those 16 dots were inaccurately marked as true. Using the dot locations, we can build a confusion matrix to express the values.