Explain what your model is for.

This is the same reference used in Technical mode. Expand it whenever you need the exact numbers, assumptions or further explanation.

Start with a decision someone needs to make

A school equipment desk wants to decide whether a model can help prioritize human review. “It is accurate” leaves too much unanswered. What does it predict, for whom, from which inputs, at what moment? What happens when it misses an urgent request? A model card records the intended use, evidence, limitations and uses the evidence does not support.

The lab begins with four fictional claims. Choose the strongest statement the evidence actually supports, then inspect the feedback. These examples are practice, separate from your saved classifier experiment.

Follow the evidence into the claim

Use this chain: task → measurements → development choices → frozen evaluation → bounded claim. Each arrow asks a question. Do the measurements represent the task? Did the results influence model selection? Are the final cases different from the selection cases? Does the claim stay within the measured population and conditions?

Suppose a team gets 18 correct predictions on 20 online requests. Its observed accuracy is 18 ÷ 20 = 0.90, or 90%. In the formula accuracy = c / n, c is the number of correct predictions and n is the number of evaluated examples. Here both are counts, and the resulting fraction has no unit. If n is zero, accuracy is undefined.

That calculation does not tell us which two errors occurred, their consequences, or how the tool performs on desk requests. A report should include the confusion counts, sample selection and missing coverage. See error costs and sampling uncertainty for experiments with those questions.

Selection changes what a result means

Training examples help fit parameters. Validation examples help choose settings and diagnose failures. A test set evaluates a frozen choice. Selecting the best of many validation scores can favor a setting that benefited from chance on that particular sample.

Freezing means committing to a choice before examining its final evaluation. It does not make a dataset representative or erase earlier exposure. If you inspect test mistakes and revise the model, results on that same set become further development or practice evidence. State the reuse and seek suitable new cases for a fresh final claim.

In the lab, the saved classifier’s test metrics remain unavailable until you explicitly freeze it in its project. The panel computes metrics from the current saved weights; it does not silently train or expose a test. A first recorded evaluation is a statement about local history, not proof that nobody has ever seen the cases.

Write five connected sections

1. Purpose: identify the task, intended user and excluded uses. The classroom classifier predicts binary labels from two synthetic coordinates; it is not a medical or credit model.
2. Data and changes: describe the source, split sizes, label noise, added examples and missing coverage. Keep prediction inputs distinct from labels available later.
3. Choice and results: say what you compared, which data informed the choice, and what the frozen evaluation found. Include counts and known reuse rather than a percentage alone.
4. Limitations: explain uncertainty, assumptions and untested conditions. “Works perfectly” is unsupported even if one small sample has no errors.
5. Appropriate use: connect the evidence to a specific permitted educational use and state what additional evidence would be needed for a different use.

The five fields in this lesson are the existing classifier project’s saved card. Editing them reopens its report completion for review. It does not change weights or erase test history. If you have not trained a classifier, write an evidence plan and mark results unknown; do not copy fictional scores into it. You can finish the claim practice without training a network.

Practice a different reporting decision

A library reminder tool scores 9/10 on email reminders and has no phone-reminder data. Before viewing the answer, write one supported result and one next evidence request.

Use the card to expose unanswered questions

For guided work, explain one result and one limitation. For engineering practice, reproduce the project’s confusion counts using its exported weights and the repository’s seeded dataset generator and provide runnable checks. For a management decision, state whether the evidence supports the proposed use, what remains missing and who should review it. These are different artifacts, not automatic qualifications awarded for filling text fields.

A field-length check establishes that writing is present. It cannot establish sound reasoning, correct code or a justified decision. The data-audit project supplies separate objective transfer cases; written artifacts and broader implementation claims still need human review. Learner pilots and human review remain pending.