In a binary classification algorithm, we have only two labels, which are True and False. Before we can calculate the accuracy, we need to understand a few key terms:
True positives: Number of observations correctly classified as True
True negatives: Number of observations correctly classified as False
False positives: Number of observations incorrectly classified as True
False negatives: Number of observations incorrectly classified as False
To calculate the accuracy, we need to divide the sum of the correctly classified observations by the number of total observations.