Accuracy is a performance metric used to evaluate classification models in machine learning. It measures the percentage of correct predictions made by a model compared to the total number of predictions.
In simple terms, accuracy tells you how often a machine learning model makes the correct prediction.
How Is Accuracy Calculated?
Accuracy is calculated using the following formula:
Accuracy = (Correct Predictions ÷ Total Predictions) × 100
For example, if a model correctly predicts 95 out of 100 instances, its accuracy is 95%.
When Is Accuracy Useful?
Accuracy is an appropriate metric when:
- The dataset is balanced.
- All classes are equally important.
- The cost of different types of errors is similar.
In these situations, accuracy provides a simple and effective way to measure model performance.
Why Can Accuracy Be Misleading?
Accuracy can be misleading when working with imbalanced datasets, where one class appears much more frequently than another.
For example, if 95% of transactions are legitimate and only 5% are fraudulent, a model that always predicts "legitimate" would achieve 95% accuracy while completely failing to detect fraud.
In such cases, metrics like precision, recall, F1-score, and ROC-AUC provide a better understanding of model performance.
Benefits of Using Accuracy
Some key advantages include:
- Easy to understand and calculate
- Useful for balanced classification problems
- Provides a quick measure of overall performance
- Commonly used for comparing classification models
Conclusion
Accuracy is a widely used metric for evaluating classification models by measuring the proportion of correct predictions. While it works well for balanced datasets, it can give misleading results for imbalanced data. Therefore, it is often combined with other evaluation metrics to provide a more complete assessment of model performance.