What does the error rate in a Confusion Matrix represent?

The error rate in a confusion matrix is defined as the proportion of incorrect predictions to the total number of predictions made. This metric captures how often the model misclassifies the data across all categories.

When analyzing a confusion matrix, it includes four key outcomes: true positives, true negatives, false positives, and false negatives. The error rate is calculated as the sum of false positives and false negatives divided by the total number of instances, which is the sum of all elements in the confusion matrix. This can be expressed as:

[ \text{Error Rate} = \frac{\text{False Positives} + \text{False Negatives}}{\text{Total Instances}} ]

Therefore, the option indicating that the error rate is represented as False/All reflects the understanding that it combines both categories of false predictions (false positives and false negatives) against the total outcomes. This is why the correct answer is accurate.