



Last update: February 19, 2014






Confusion Matrix
True positives (TP), true negatives (TN),
false positives (FP), and false negatives (FN), are the four different possible outcomes of a single prediction for a
binomial classification task with classes “1” (“yes”) and “0” (“no”). A
false positive is when the outcome is incorrectly classified as “yes” (or “positive”), when it is in fact “no” (or “negative”). A
false negative is when the outcome is incorrectly classified as negative when it is in fact positive.
True positives and true negatives are obviously correct classifications.
Keeping track of all these possible outcomes is such an errorprone activity, that they are usually shown in what is called a
confusion matrix.
In Classification, Logistic Regression and Logic Synthesis problems, GeneXproTools
shows the Confusion Matrix for all the evolved models and updates it continuously during a run.
And in the Results Panel of Classification, Logistic Regression and Logic Synthesis runs, GeneXproTools also evaluates the Confusion Matrix for the validation/test dataset.
And finally, in the Logistic Regression Window, GeneXproTools shows a more comprehensive Confusion Matrix both for the training and validation datasets.
Besides the usual Confusion Matrix, GeneXproTools also shows in the Logistic Regression Window a graphical representation of the confusion matrix accounting for the distribution of the different outcomes by bin. This innovative confusion matrix is called
Distribution Confusion Matrix.
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