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Exercises

Page Maps

graph LR
  family["Reproducible Research"]
  program["Deep Dive DVC"]
  section["Experiments Baselines Controlled Change"]
  page["Exercises"]
  capstone["Capstone evidence"]

  family --> program --> section --> page
  page -.applies in.-> capstone
flowchart LR
  orient["Orient on the page map"] --> read["Read the main claim and examples"]
  read --> inspect["Inspect the related code, proof, or capstone surface"]
  inspect --> verify["Run or review the verification path"]
  verify --> apply["Apply the idea back to the module and capstone"]

Use these exercises to practice controlled exploration, not only DVC command vocabulary.

The strongest answers will explain the baseline, the candidate intent, the declared change, the comparison evidence, and the promotion or discard decision.

Exercise 1: Name the baseline

You have a published baseline with:

evaluate:
  threshold: 0.65

and:

{
  "f1": 0.81,
  "precision": 0.78,
  "recall": 0.84
}

Write a short baseline description that explains what state the candidate experiments will compare against and which evidence files a reviewer should inspect.

Exercise 2: Scope a candidate

Suppose you want to try:

  • lower evaluate.threshold from 0.65 to 0.50
  • switch fit.model_family from logistic regression to tree boosting
  • remove weekends from the evaluation data

Decide whether this should be one candidate run or separate candidate runs.

Explain your reasoning.

Exercise 3: Interpret a candidate table

You see:

candidate                   threshold    f1     precision    recall
baseline                    0.65         0.81   0.78         0.84
lower-threshold-for-recall  0.50         0.84   0.75         0.95

Write a review note that explains:

  • what changed
  • what improved
  • what got worse
  • what release objective would make the candidate promising

Exercise 4: Identify what DVC experiments do not prove

A teammate says:

We used dvc exp run, so the winning candidate is automatically valid.

Write a response that explains what DVC experiments help record and what still requires human review.

Exercise 5: Decide promotion or discard

A candidate changes evaluate.threshold from 0.65 to 0.50.

It improves recall, reduces precision, keeps the same reported evaluation population size, and matches the release objective of reducing missed escalations.

Describe:

  • what you would inspect before applying the candidate
  • what you would check after applying it
  • what a strong promotion note should include

Mastery check

You have a strong grasp of this module if your answers consistently keep five ideas visible:

  • a baseline anchors comparison
  • a candidate should have a focused intent
  • DVC experiments preserve candidate evidence without replacing review judgment
  • selection should describe tradeoffs, not only best metrics
  • promotion should happen only after applying, inspecting, and committing an intended state