In the realm of machine learning, true negatives (TNs) are one of the vital results that data scientists use to evaluate the accuracy of their models. TNs are the number of times that a model correctly predicts a negative result. Simply put, TN represents the number of instances where the model predicts that something is not present, and it is indeed not present.

To define TNs, it is necessary to understand the concept of binary classification problems. In binary classification, there are only two possible outcomes of an event, positive or negative. For instance, in spam email detection, the email can be classified as either spam or non-spam. Hence, a binary classification problem has two classes, where one is known as positive, and the other is negative.

When we apply machine learning algorithms to binary classification problems, each prediction is either true or false. Predictions that are true are either because they are true positives (TP) or because they are true negatives (TN). True negatives represent the correct predictions of the model that something is not present. They are the instances where the model predicts a negative class, and the prediction is correct.

For example, let us consider a cancer detection model that classifies a tumor as either malignant or benign. If the model identifies the tumor as benign, and the tumor is benign, then this is a true negative. It means that the model successfully identified the absence of cancer in the patient.

The accuracy of a machine learning model is measured by various metrics, one of which is known as the accuracy score. The accuracy score is the ratio of the correct predictions to the total number of predictions made. In other words, the accuracy score shows the percentage of time that the model is correct in its predictions. Therefore, the more true negatives a model has, the higher its accuracy score would be.

In conclusion, true negatives play a crucial role in evaluating the performance of a machine learning model. They represent the number of instances where the model correctly predicts a negative class. It is essential to understand that increasing the true negatives of a model will increase its accuracy score, leading to a more reliable and robust model.