Artificial Intelligence & Machine Learning
Accuracy
Definition
In classification tasks, accuracy is a metric that measures the number of correct predictions made by a model as a percentage of the total number of predictions.
Why It Matters
Accuracy is the most intuitive and common metric for evaluating a classification model. It gives a simple, overall score of how well the model is performing.
Contextual Example
If a model correctly identifies 95 out of 100 images, its accuracy is 95%.
Common Misunderstandings
- Accuracy can be misleading on imbalanced datasets. If a disease occurs in only 1% of the population, a model that always predicts "no disease" would have 99% accuracy but would be completely useless.
- For this reason, other metrics like precision, recall, and F1-score are often more informative than accuracy alone.