Description: The term ‘True Negative’ refers to an outcome in the context of supervised learning, where a classification model correctly predicts the negative class of a dataset. In other words, it is an instance where the model appropriately identifies that an example does not belong to the class of interest. This concept is fundamental in evaluating the performance of machine learning models, as it helps measure the accuracy and effectiveness of the model in identifying classes. True negatives are part of the confusion matrix, which also includes true positives, false positives, and false negatives. The proportion of true negatives relative to the total number of actual negative examples is crucial for calculating metrics such as specificity and precision of the model. A high number of true negatives indicates that the model is effective in identifying examples that do not belong to the positive class, which is especially relevant in applications where the consequences of false positives can be significant, such as in medical diagnoses or fraud detection. In summary, true negatives are an essential component in the evaluation of supervised learning models, providing valuable information about their ability to correctly classify negative instances.
