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True negatives, in machine learning, are one component of a confusion matrix that attempts to show how classifying algorithms work.
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.