Accuracy (machine learning)

The number of correct classification predictions divided by the total number of predictions.¹ That is:

$$\text{Accuracy} = \frac{\text{correct preds} + \text{incorrect preds}}{\text{correct preds}}$$

Binary classification provides specific names for the different categories of correct predictions and incorrect predictions. So, the accuracy formula for binary classification is as follows:¹

$$\text{Accuracy} = \frac{TP + TN}{TP + TN + FT + FN}$$

where:

• TP is the number of true positives (correct predictions).
• TN is the number of true negatives (correct predictions).
• FP is the number of false positives (incorrect predictions).
• FN is the number of false negatives (incorrect predictions).

When to use

Because it incorporates all four outcomes from the confusion matrix (TP, FP, TN, FN), given a balanced dataset, with similar numbers of examples in both classes, accuracy can serve as a coarse-grained measure of model quality. For this reason, it is often the default evaluation metric used for generic or unspecified models carrying out generic or unspecified tasks.²

However, when the dataset is imbalanced, or where one kind of mistake (FN or FP) is more costly than the other, which is the case in most real-world applications, it’s better to optimize for one of the other metrics instead.²

See also

• Precision
• Recall (a.k.a. probability of detection)
• False positive rate (a.k.a. probability of false alarm)

Footnotes

1. developers.google.com/machine-learning/glossary#accuracy ↩ ↩²

2. ML crash course - Classification ↩ ↩²