👉 TN, or True Negative, refers to the subset of data points in a binary classification problem that do not belong to the positive class. In the context of machine learning, TN represents all the instances where the model predicts the negative outcome correctly. This category is crucial for evaluating the performance of classification models, as metrics like accuracy, precision, and recall are calculated based on TN, alongside the false positive rate (FP). By analyzing TN, data scientists can better understand how well their models distinguish between the two classes and identify potential biases or areas for improvement.
