In a Confusion Matrix, how is Accuracy determined?

In the context of a Confusion Matrix, accuracy is defined as the proportion of correctly classified instances (both true positives and true negatives) out of the total instances examined. This can be mathematically represented as:

Accuracy = (True Positives + True Negatives) / (Total Instances)

Total Instances is the sum of all four components of the Confusion Matrix: true positives, true negatives, false positives, and false negatives. Therefore, accuracy reflects the overall ability of the model to correctly identify both the positive and negative classes.

The choice regarding True/All aligns closely with this definition since "True" can pertain to both true positives and true negatives, while "All" refers to the overall total instances. This relationship captures the essence of accuracy, focusing on the correct predictions relative to all instances observed.

Understanding accuracy in this manner is crucial because it provides insights into the effectiveness of the classification model. A high accuracy indicates that the model is performing well, while a low accuracy suggests room for improvement.