Train and freeze a simple model
See this course’s project requirements
Evaluate the fictional box classifier and document its evidence and use boundaries in a model card.
Keep your working files in your own notebook or software. Save a private description, checklist, and work link in My Proof.
What to include
- Define the prediction task, baseline and human decision boundary.
- Document feature/label meaning and independent training/evaluation records.
- Compare two thresholds and freeze the chosen model.
- Calculate confusion counts, accuracy, precision and recall against a baseline.
- Report the separate condition audit and a targeted next evaluation step.
Review and revise
Use these criteria to check your work and choose what to improve.
- Concepts: features, labels, training and decisions remain distinct.
- Evaluation: arithmetic and denominators match the original records.
- Judgment: small samples and uneven performance are not overstated.
- Accountability: use boundaries and further evidence needs are concrete.
Want more background?
The idea in everyday life
Training does not have to involve a large computer. A simple model can be selected by comparing a few candidate rules. This makes the logic visible and helps explain why a perfect training score is not a guarantee. You will choose between two thresholds using the four prepared records, then freeze that choice before looking at the held-out results.
Training chooses a model using known examples; evaluation checks it on records not used for that choice.
One step at a time.
Use the sample or information you have permission to use. Keep your work as you go. You can pause between steps.
- 1
Define candidate A as predict yes when scuffs >= 3 and candidate B as predict yes when scuffs >= 5. Both predict no below their threshold; neither learns any other feature.
- 2
Apply both rules to R1=0/no, R2=2/no, R3=3/yes and R4=5/yes. Record a prediction beside each known label before counting correct outcomes.
- 3
Candidate A gets all four correct. Candidate B gets three correct because it predicts no for R3. Choose A under this exercise’s explicit rule of fewer training mistakes among these two candidates.
- 4
Write "threshold=3, frozen before evaluation" in the model record. Do not silently change the threshold after seeing the final test labels; doing so would turn those labels into tuning information.
- 5
Overfitting means matching details in the training examples that do not work well on new examples. Even this simple model needs fresh tests; four correct training predictions tell us little about future performance.
See how it works.
Fictional training comparison: at scuffs 0 and 2, both thresholds predict no and match the labels. At scuffs 3, threshold 3 predicts yes correctly while threshold 5 predicts no incorrectly. At scuffs 5, both predict yes correctly. Candidate A therefore scores 4/4 and B scores 3/4 on the training rows. The selection rule chooses A and locks its threshold at three. This is a tiny model-fitting procedure. The perfect training score describes these four cases only; it does not establish that scuffs fully explain damage or that the same rule will perform perfectly on fresh boxes.
Did you cover these?
- Both candidates are scored on the same four rows.
- Threshold 3 wins 4/4 versus 3/4.
- The selected threshold is frozen before evaluation.
What to finish
A candidate comparison table and a frozen threshold record.
Watch for this
Using test outcomes repeatedly to improve a model while still calling them an untouched test.
How can I tell I understand it?
Explain model selection and why a separate evaluation remains necessary.
Quick knowledge check
Answer all 3 questions. Get at least 2 right and the lesson completes automatically.
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Answer all 3 questions above and get at least 2 right. You can review the explanations and try again.
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Sources and further reading
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Source & access notes · checked 2026-09-10
Read MIT’s public overview of learning problems, representation, overfitting and generalization. Optional deeper study is more mathematically demanding than this original beginner course.
Access: MIT states the Open Learning Library materials are free to use without enrolling; an optional account tracks progress. This Life Starter course provides no MIT enrollment, credit, certificate or affiliation.
Reuse: Linked for reference. Examples, activities and assessments here are original; free access alone does not grant permission to reuse source text or branding.
Source & access notes · checked 2026-09-10
Read train/validation/test separation, representative evaluation and duplicate-example risks. Original box fixtures are used rather than source exercises.
Access: Free public reading; no account or certificate is required for this Life Starter course.
Reuse: Linked for reference. Examples, activities and assessments here are original; free access alone does not grant permission to reuse source text or branding.
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