Deciding whether ML is appropriate
Learn the reasoning. Try the task. Compare your answer.
Prepared for DataCareerHub · Curriculum version 2026-10-15.1
By the end of this lesson
- Apply deciding whether ML is appropriate to the fictional case.
- Record the rule, evidence and validation result.
- Explain a limitation and a defensible next step.
Fictional learning case · Local practice files · No personal data.
Understand the method
Deciding whether ML is appropriate
ML is appropriate when a useful prediction target exists, examples are representative enough, inputs arrive in time and evaluation can reflect the real decision. A model also needs a plausible action: predicting an outcome nobody can influence or review may add complexity without benefit. Compare against simpler alternatives and consider the cost of errors, maintenance and monitoring.
Work through the case
Cedar’s fictional review alert could help an operations team prioritise attention, but this exercise does not demonstrate that alerts improve delivery. The dataset is small and generated from a known teaching process. It omits operational disruptions, repeated-customer structure and real-world drift. Performance here demonstrates a method, not a commercial service claim.
A method you can reproduce
Complete suitability_checklist.txt with target availability, feature timing, sample coverage, baseline, error cost, human review and monitoring. Specify a no-ML alternative, such as a distance-based rule or historical rate table. State conditions under which you would stop: unreliable labels, leaked features, no action, harmful use or no advantage over a baseline. Define who owns a future decision and how errors can be challenged.
Check your reasoning
A model is not justified by the availability of a library or an impressive score. Avoid high-stakes decisions about employment, credit or health using this teaching example. The course uses a low-stakes operational case and makes no certification or deployment approval claim.
Further reading: NumPy least squares, Preventing leakage, Cross-validation. These official references support the methods; the case and exercises are authored for DataCareerHub.
Practical exercise
Write a proceed/pause note for the Cedar experiment and a separate note for real deployment. List three missing pieces of evidence and a simpler comparator.
Write your answer in learner_worksheet.txt and retain the evidence file. Attempt this before opening the solution.
Open the worked solution
Proceed with the local learning experiment because timing, labels and evaluation are explicit. Pause real deployment pending representative operational data, action-impact evidence, error-cost agreement, review procedures and monitoring. A train-only mean/majority baseline is the minimum comparator; a simple operational rule is an additional business comparator.
Independent extension: change one relevant input in a separate copy. Predict the result first, rerun the checks and explain any difference. Keep the supplied source unchanged.
One more question.
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Continue to Module 02 →This is a fictional learning case. Results do not establish real-world performance or causal effects. Portfolio practice is not a claim of client work or an accredited qualification. Explore practical lessons, worked examples and projects. Follow a published path, practise the method and build work you can explain.