True negative (TN): Difference between revisions

The true negative rate is dependent upon factors such as the quality and quantity of training data, model complexity, and hyperparameter selection. If these training data are insufficiently representative of actual test data, a model might struggle to accurately predict negative data points. Furthermore, if it's too simple, it might not capture all features that distinguish negative from positive data points; conversely, if it's too complex it could overfit and perform poorly on actual testing data.

Machine learning relies on computer programs to determine whether something is good or bad. If the program says something is bad and it turns out to be true, we call that a "true negative." This indicates the program has done an effective job at warning us when something is detrimental. As such, accuracy in its judgments must be ensured in order for us to trust it with accurate predictions about whether something should be praised or avoided.