If you’ve ever wrestled with choosing the right metric for your model, you’re not alone. Accuracy alone can be misleading, especially when models are put to work making real decisions.
Setting Up Models
Whether you’re working on a classification task or a regression problem, setting up your models correctly is key. For instance, you might use a logistic regression to predict if a striker will score next match, while a gradient boosting regressor estimates how many goals a player might net. Handling these scenarios separately with simple, transparent models helps ensure you’re measuring performance the right way.
Classification: Beyond Accuracy
Many projects stop at accuracy, but that doesn’t always tell the whole story—especially with imbalanced data. If you’re trying to decide whether a player will score, consider digging into metrics like Receiver Operating Characteristic – Area Under the Curve (ROC-AUC), Precision-Recall AUC (PR-AUC), Log Loss, and the Brier Score. For example, ROC-AUC can reveal how well your model ranks positive results over negatives, offering a more nuanced insight than accuracy alone.
Log Loss
When your model outputs probabilities, Log Loss comes into play. This metric penalises overconfident mispredictions, which is particularly useful in showing the true performance of your model on imbalanced datasets.
Regression: Advanced Evaluation
For regression tasks, accuracy isn’t the right yardstick. Instead, metrics like Root Mean Squared Error (RMSE), R² (which tells you how much of the variance is explained by your model), Root Mean Squared Logarithmic Error (RMSLE) for skewed data, and Quantile Loss provide a clearer picture of model effectiveness. These measures help you understand and minimise errors, offering a deeper insight into the certainty and quality of your predictions.
Conclusion
Creating models that people can trust goes beyond just hitting a high accuracy number. In classification, that means tackling imbalanced data and ensuring probability outputs are well-calibrated. For regression, it’s all about reducing error and grasping the uncertainty in predictions. By focusing on advanced evaluation metrics, you can build models that are not only accurate on paper but also genuinely ready for real-world decision making.
