True Negatives
In the context of binary classification, True Negatives (TN) are the cases where the actual class of the data point is negative and the predicted class is also negative. In other words, the model correctly identifies the absence of the target condition. True Negatives are an important evaluation metric for binary classification models, especially when the negative class is the majority class in the dataset. A high number of True Negatives indicates that the model is performing well in identifying the negative class, which is crucial in many real-world applications.
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