Machine Learning in Java
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Precision and recall

The solution is to use measures that don't involve true negatives. Two such measures are as follows:

  • Precision: This is the proportion of positive examples correctly predicted as positive (TP) out of all examples predicted as positive (TP + FP):

  • Recall: This is the proportion of positives examples correctly predicted as positive (TP) out of all positive examples (TP + FN):

It is common to combine the two and report the F-measure, which considers both precision and recall to calculate the score as a weighted average, where the score reaches its best value at 1 and worst at 0, as follows: