What does a confusion matrix summarize?

A confusion matrix is a vital tool in evaluating the performance of a classification model. It summarizes the outcomes of predictions made by the model against the known true classifications, allowing practitioners to see how many instances were correctly classified and how many were misclassified.

The matrix categorizes predictions into four main outcomes: true positives (correctly predicted positive instances), true negatives (correctly predicted negative instances), false positives (incorrectly predicted positive instances), and false negatives (incorrectly predicted negative instances). By reviewing these values, a more detailed performance analysis can be conducted, including calculating key metrics such as accuracy, precision, recall, and F1 score.

The other options focus on different aspects of AI and model training that are unrelated to how a confusion matrix operates. For instance, it does not deal with the cost of implementing AI solutions, nor does it reflect the amount of data required for training, or the structural layout of neural networks. Hence, the confusion matrix's primary and defining role is indeed to summarize the correct and incorrect predictions of a classification model.