Classifier accuracy

• categories: Data Science, Interview Question

The formula for classifier accuracy is:

$Accuracy=\frac{\text{Number of Correct Predictions}}{\text{Total Number of Predictions}}\times 100$

Where:

• Number of Correct Predictions: The sum of true positives (TP) and true negatives (TN).
• Total Number of Predictions: The total number of samples, which is the sum of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN).

$Accuracy=\frac{TP+TN}{TP+TN+FP+FN}\times 100$

Example:

If a classifier correctly predicts 90 samples out of 100, its accuracy would be 90%