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Module 2 · Cleaning & Feature Engineering · Recap
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Capstone: end-to-end ML pipeline
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📝 **Question:** Which ORDER of steps is the production-safe ML workflow? 📋 Pick the right answer. 💡 **Hint:** Re-read the theory above if unsure.
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📝 **Question:** Which ORDER of steps is the production-safe ML workflow? 📋 Pick the right answer. 💡 **Hint:** Re-read the theory above if unsure.
A
`model.fit(X_raw, y)` first to get a working baseline, then clean and engineer features later as accuracy improvements
B
Load → QA → clean → features → stratified split → Pipeline → cross-validate → touch test set once → persist artifact (prevents leakage)
C
Skip cross-validation, trust a single `train_test_split(random_state=0)` and ship whichever fit reaches the target metric first
D
Tune hyperparameters by reading the test-set F1 after every iteration and stop when the test metric stops improving notably
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