Evaluation Metrics in Machine Learning

Evaluation metrics are vital for determining how well machine learning models perform and whether they meet real-world objectives. They provide quantitative measures that guide model selection, tuning, and deployment.

Classification Metrics

• Accuracy: Measures overall correctness but can mislead with imbalanced data.
• Precision: Focuses on the proportion of true positives among predicted positives; important when false positives are costly.
• Recall (Sensitivity): Captures the proportion of true positives among actual positives; critical when missing positives is dangerous.
• F1-Score: Harmonic mean of precision and recall, balancing both.
• ROC-AUC: Evaluates the classifier’s ability to distinguish between classes across thresholds.

Regression Metrics

• Mean Absolute Error (MAE): Average absolute difference between predictions and actual values; easy to interpret.
• Mean Squared Error (MSE): Penalizes larger errors more heavily, highlighting outliers.
• Root Mean Squared Error (RMSE): Square root of MSE, keeping units consistent with the target variable.
• R² (Coefficient of Determination): Explains how much variance in the target is captured by the model.

Clustering Metrics

• Silhouette Score: Assesses how well clusters are separated and cohesive.
• Adjusted Rand Index (ARI): Compares clustering results against ground truth labels.
• Davies–Bouldin Index: Measures average similarity between clusters; lower values indicate better separation.

Choosing the Right Metric
The choice of metric depends on the application context. For example, in medical diagnosis, recall is prioritized to avoid missing cases, while in spam filtering, precision matters to prevent misclassifying important emails. Metrics thus ensure models are not only mathematically sound but also practically reliable.