What does a confusion matrix display?

A confusion matrix is a valuable tool in the field of machine learning, particularly in classification tasks. It is a table that is used to describe the performance of a classification model by visualizing the actual versus predicted classifications. The matrix provides insights into how well the model is performing, showing the number of true positives, false positives, true negatives, and false negatives.

The accuracy of model predictions can be derived from a confusion matrix, as it allows one to compute various metrics such as precision, recall, and accuracy itself. Accuracy is calculated by taking the sum of the true positives and true negatives divided by the total number of instances. This metric is crucial for understanding how effectively a model is making predictions and where it may be falling short.

In summary, a confusion matrix provides detailed information that goes beyond simple accuracy by showing the specific types of errors and correct predictions, thus helping practitioners to evaluate and improve their models effectively.