I recently completed a module on evaluation metrics that highlighted a common pitfall: over-relying on accuracy for imbalanced datasets.
Working with a customer churn dataset (73% non-churners, 27% churners), I built a model with 80% accuracy. A dummy model that always predicts “no churn” achieved 73%. The improvement was marginal.
The deeper issue: precision was 67% and recall was 54%. The model missed nearly half of actual churners—information completely obscured by the accuracy metric.
Key takeaways:
<ul>
<li>Confusion matrices decompose performance into TP, FP, TN, FN</li>
<li>Precision/recall reveal different failure modes</li>
<li>ROC-AUC provides threshold-independent evaluation</li>
<li>Cross-validation gives confidence intervals, not point estimates</li>
<li>Metric choice should align with business costs</li>
</ul>
The module uses the Telco Customer Churn dataset from Kaggle and walks through implementing these metrics in sklearn.
📝 My full notes: <a href="https://medium.com/@meediax.digital/11f16e770ef2?source=friends_link&amp;sk=1b3e8267628ae7ab2365f602d3da75c4" target="_blank" rel="noopener nofollow">Medium</a>
🎓 Free Course: <a href="https://datatalks.club/blog/machine-learning-zoomcamp.html" target="_blank" rel="noopener nofollow">ML Zoomcamp</a>
Pro tip: Always build a baseline/dummy model first. If your fancy ML model is barely better, something’s wrong with either your features, your metric, or both.
Anyone else working through ML Zoomcamp? The hands-on approach with real datasets is 🔥

Austin Deyan

Accuracy can be misleading for imbalanced datasets. Using a customer churn dataset with 73% non-churners, a model with 80% accuracy only marginally outperformed a dummy baseline at 73%. The real issues emerged through precision (67%) and recall (54%), showing the model missed half of actual churners. Better evaluation approaches include confusion matrices, precision/recall metrics, ROC-AUC for threshold-independent assessment, and cross-validation for confidence intervals. Always build baseline models first and align metrics with business costs.

Why accuracy is misleading for imbalanced classification (ML Zoomcamp Module 4)