Testing a Machine Learning (ML) model is very different from testing traditional software because:
The output is probabilistic, not deterministic.
The behavior depends on data patterns, not just logic.
To test an ML model effectively, you need a multi-layered strategy combining functional, data-driven, and performance-based testing.
✅ 1. Unit Testing the ML Pipeline (Code-Level)
🔍 What to Test:
Data preprocessing methods (normalization, encoding)
Feature extraction logic
Model loading and inference function
💡 Example:
