True Negatives

What Does True Negatives Mean?

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.

Techopedia Explains True Negatives

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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Margaret Rouse is an award-winning technical writer and teacher known for her ability to explain complex technical subjects to a non-technical, business audience. Over the past twenty years her explanations have appeared on TechTarget websites and she's been cited as an authority in articles by the New York Times, Time Magazine, USA Today, ZDNet, PC Magazine and Discovery Magazine.Margaret's idea of a fun day is helping IT and business professionals learn to speak each other’s highly specialized languages. If you have a suggestion for a new definition or how to improve a technical explanation, please email Margaret or contact her…