A Guide to Model Evaluation Metrics
Choosing the right evaluation metric is critical for comparing models and selecting the best one for your problem.
Classification Metrics
- Accuracy measures the fraction of correct predictions. It works well when classes are balanced but can be misleading with imbalanced datasets.
- Precision and recall capture how well the model retrieves relevant instances without producing too many false positives or negatives. The F1-score provides a balance between the two.
Regression Metrics
- Mean Absolute Error (MAE) evaluates the average magnitude of errors.
- Root Mean Squared Error (RMSE) penalizes larger errors more heavily, making it useful when large deviations are particularly undesirable.
Selecting evaluation metrics that align with business goals will help you make informed decisions about which model to deploy.
