True negatives indicate that a machine
learning program has been set on test data where there is an outcome termed
negative that the machine has successfully predicted.
Take the typical confusion matrix, which consists of true positives, false positives, true negatives and false negatives. The true negatives would be the negative cases in which the machine learning program has guessed at the “negative” classification correctly.
For instance,
using a one and a zero as positive and negative classes or types, if the true
positive identifies a one successfully, the true negative successfully
identifies a zero.
These types of
confusion matrices are widely treated in classification algorithm projects.
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