Section checkpoint

Start Here

6 questions drawn from the 2 chapters in this section. If most land on the first try, you are ready for the next section. If not, the explanation after each answer says which idea to revisit.

Checkpoint

6 questions · first-try answers earn the most XP

1. A product scores with a small model and then explains with an LLM. Why is that a good shape?

2. What is the honest summary of classical ML versus generative AI?

3. Why is a small trained model often *cheaper* than a prompted LLM for high-volume prediction?

4. What does calling `model.score(Xte, yte)` actually return in scikit-learn?

5. What does the loop load → split → fit → score represent?

6. What happens to your results if you omit `random_state`?

What this checkpoint covers

Classical ML vs Generative AI
When a small trained model beats a big prompted one.
Your ML Setup: scikit-learn in 10 Minutes
Install, load a dataset, fit your first model.

Next section: Data First