Beginner · Evaluation

Accuracy

Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.

TL;DR. The fraction of predictions a model gets correct on a labeled dataset.

Technical Definition

The fraction of predictions a model gets correct on a labeled dataset.

How it works

Accuracy = (correct predictions) / (total predictions). Simple and intuitive, but misleading when classes are imbalanced — predicting 'no fraud' on every transaction can achieve 99% accuracy while being useless. Pair it with precision, recall, and F1 for a fuller picture.

Related Concepts

• Precision & Recall — Complementary metrics measuring classifier accuracy on positive predictions (precision) and ability to find all positives (recall).
• F1 Score — The harmonic mean of precision and recall, balancing both into a single number.
• Confusion Matrix — A table showing true vs predicted labels for every class.