Learn AIExplain what your model is for.
Turn results into bounded claims, identify missing evidence and draft a model card linked to your actual experiment.
By the end: Distinguish development, frozen evaluation and reused evidence; state intended use, limitations and next evidence in a model card.
Start with something concrete.
A team says its model is 95% accurate. Was that the best result after trying many settings? Which examples were included? Before trusting the claim, inspect the evidence and narrow the wording to what it supports.
Try the interactive model ↓A team chooses the best of 12 thresholds on one validation set. What does the winning score establish?
Change one thing. Watch what follows.
- Choose a claim for each of the four fictional evidence cases before checking feedback. Explain which word makes an unsupported claim too strong.
- Inspect the saved classifier evidence. Identify whether it describes initialized weights, development results or a frozen test. Do not train merely to open this lesson.
- Draft the five shared model-card fields if you have a classifier experiment. Otherwise outline the evidence you would need, clearly marking unknown results. These edits update the existing project card.
- For a decision brief, name an unsupported use and the evidence needed before reconsidering it. For implementation practice, reproduce confusion counts using exported weights and the repository dataset generator and document your checks.
Loading this browser experiment…
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
- 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.
- Data and changes: describe the source, split sizes, label noise, added examples and missing coverage. Keep prediction inputs distinct from labels available later.
- 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.
- Limitations: explain uncertainty, assumptions and untested conditions. “Works perfectly” is unsupported even if one small sample has no errors.
- 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.
Compare your reasoning
“Nine of ten evaluated email reminders were correct” reports the observed result with its denominator. Phone performance remains unknown. Request a suitable phone-reminder sample, define the error types and consequences, and commit to the evaluation procedure before comparing results. The small email sample also warrants uncertainty; 90% is not a guarantee.
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.
A tool scored 18/20 on online requests. No desk requests were sampled. Which report is supported?
Take this idea with you.
A useful model card connects a specific use to traceable evidence and explicit limits. Writing a complete card does not validate its reasoning or authorize a deployment.
Put the lesson into practice: Open the hands-on project and independent transfer assessment. Lesson completion and this practice check are separate from project submission and assessment results.