I’ve continued my journey in the Machine Learning Zoomcamp by Alexey Grigorev — and just finished Section 4: Evaluation Metrics for Classification. 🎯

This module was about learning how to properly evaluate classification models and deal with imbalanced datasets. This is an essential step before taking models to production!

Here’s what I covered this week:
1️⃣ Accuracy, Precision, Recall, and F1-Score
2️⃣ ROC Curves & AUC
3️⃣ Cross-Validation
4️⃣ Confusion Matrices
5️⃣ Handling Class Imbalance

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