Classification problems rely on several metrics to evaluate model performance, each addressing different aspects of prediction quality. The most common metrics include accuracy, precision, recall, F1 score, ROC-AUC, and log loss. These metrics help developers understand how well a model identifies correct classes, balances errors, and handles probabilistic outputs. Choosing the right metric depends on the problem’s requirements, such as minimizing false positives or prioritizing class balance.
Accuracy and Confusion Matrix Accuracy measures the proportion of correct predictions (both true positives and true negatives) out of all predictions. While intuitive, it can be misleading in imbalanced datasets. For example, in fraud detection where 99% of transactions are legitimate, a model predicting “not fraud” every time would have 99% accuracy but fail to detect fraud. The confusion matrix breaks down predictions into true positives, false positives, true negatives, and false negatives, providing a foundation for other metrics like precision and recall. Developers often start here to identify where the model struggles, such as high false negatives in medical diagnoses.
Precision, Recall, and F1 Score Precision (true positives / (true positives + false positives)) focuses on minimizing false positives. It’s critical when incorrectly flagging harmless cases is costly, like spam detection where misclassifying legitimate emails as spam harms user trust. Recall (true positives / (true positives + false negatives)) emphasizes minimizing false negatives, crucial in medical testing where missing a disease could be fatal. The F1 score, the harmonic mean of precision and recall, balances both. For instance, in a cancer screening model, a high F1 score ensures the model neither misses too many cases (low recall) nor overdiagnoses (low precision). These metrics are often used together to address trade-offs.
ROC-AUC and Log Loss The Receiver Operating Characteristic (ROC) curve plots the true positive rate against the false positive rate across classification thresholds. The Area Under the Curve (AUC) quantifies the model’s ability to distinguish classes, with 1.0 indicating perfect separation. For example, in credit scoring, a high AUC means the model effectively ranks high-risk applicants higher than low-risk ones. Log loss measures the difference between predicted probabilities and actual labels, penalizing overconfident incorrect predictions. In weather forecasting, log loss evaluates how well the model’s probabilistic outputs (e.g., 80% chance of rain) align with reality. These metrics are particularly useful for probabilistic models and scenarios requiring nuanced threshold tuning.