Accuracy is the most straight-forward evaluation metric for a classification problem, and it simply measures the overall proportion of observations that were correctly classified. The accuracy can be calculated from a confusion matrix by summing the diagonal cells (true positives and true negatives) and then dividing by the total number of observations. In general, accuracy is calculated by:
Accuracy = (True Positives + True Negatives) / Total Observations
Using an example:
Accuracy is = (100 + 150) /360 = .694
