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In binary classification, a true negative (TN) is one of the four possible outcomes when evaluating the performance of a classification model. These outcomes are based on comparing the model's predictions with the actual ground truth.

Here are the four possible outcomes in binary classification:

**True Positive (TP)**: The model correctly predicts the positive class when the true class is indeed positive.**True Negative (TN)**: The model correctly predicts the negative class when the true class is indeed negative.**False Positive (FP)**: The model incorrectly predicts the positive class when the true class is actually negative. This is also known as a Type I error.**False Negative (FN)**: The model incorrectly predicts the negative class when the true class is actually positive. This is also known as a Type II error.

A true negative (TN) occurs when the model correctly identifies instances of the negative class. It means that the model successfully recognizes cases where the true class is negative, and it doesn't erroneously classify them as positive.

The terms true positive, true negative, false positive, and false negative are commonly used to calculate various performance metrics for classification models, such as accuracy, precision, recall, and F1 score. These metrics help assess the model's effectiveness in making correct predictions and avoiding misclassifications.