Definition
Accuracy is often used in classification problems: for example, when a model determines whether an email is spam or a regular email. If out of 100 examples the model correctly recognized 90, its accuracy is 90%. But accuracy by itself is not always sufficient: for rare events, the model may look good, although it misses the most important cases.
Beispiel
In the task of finding fraudulent transactions, a model may almost always say “everything is fine” and have a high percentage of correct answers, but still miss real attacks.
Warum es wichtig ist
Understanding accuracy helps you not to believe one beautiful number and correctly compare AI tools, especially when the cost of an error is different for different classes.
So funktioniert es
The metric is calculated as the ratio of correct answers to the total number of verified examples. For an honest assessment, a separate test sample is used, which the model did not see during training.
Wo es genutzt wird
- classifier evaluation
- comparison of models
- AI service quality control
Einschränkungen
Precision is poorly suited for unbalanced data and problems where one error is more dangerous than another. Additional metrics are often needed: recall, positive class precision, F1 or AUC.
FAQ
Why is “Accuracy” useful to know?
Understanding accuracy helps you not to believe one beautiful number and correctly compare AI tools, especially when the cost of an error is different for different classes.