A beginner’s guide to evaluating machine learning models beyond aggregate metrics

Evaluating machine learning models solely based on aggregate metrics like accuracy or F1-score can be misleading, as these metrics provide a limited view of model performance and can obscure underlying issues such as reliance on spurious data. To overcome this, the article suggests expanding model evaluation processes to include benchmarks, data cohort analysis, and explainability techniques. Benchmarks serve as goalposts, helping to contextualize model performance against existing systems or simpler models, while data cohort analysis reveals underperforming subpopulations that aggregate metrics might hide. Explainability techniques, such as LIME or SHAP, help uncover which features influence model predictions, ensuring that models rely on meaningful patterns rather than noise. By employing these methods, practitioners can gain a deeper understanding of model quality and address potential issues that could affect deployment and reliability.