Write a model card for a portfolio project
In this article (6 sections)
A model card helps a reader understand what a model was built to do, how it was evaluated and where its evidence ends. It should summarize an actual experiment, with links to supporting artifacts, rather than read like a product advertisement.
Our completed example card documents a synthetic seven-day inactivity model. Its status is educational reference with review pending. It has not been deployed or shown to improve a real customer outcome.
Lead with intended use and exclusions
The model predicts whether an account has zero active days during a specified future week. That is not the same as cancellation. It also does not estimate whether a reminder would change behavior.
The card states that the data are320 authored conditional snapshots across forty fictional identities, not real accounts or a continuous event reconstruction. This prevents a reader from interpreting the measured scores as a commercial benchmark.
The Model Cards for Model Reporting paper provides the primary reporting framework. The card here applies that idea to an original teaching case and its actual evidence.
Connect model details to reproducible artifacts
Name the target, features, preprocessing, estimator, selected hyperparameters and fitted-data boundary. The example uses numeric median imputation, missingness indicators, scaling, one-hot encoding and logistic regression with C=0.1.
Its contract identifies the label horizon, reporting delay, candidate grid and threshold. Its evidence manifest identifies inputs, code and outputs by hash. Its clean-environment report records reproduction of predictions within a declared tolerance.
These details let a reviewer locate the implementation without turning the card into a dump of every package or source file. Keep the detailed inventory linked and summarize the decisions that affect interpretation.
Report the result that could change a decision
The selected model improves test log loss from the baseline's0.754582 to0.702749. At threshold0.5, however, it identifies only22 of55 positive snapshots: recall40%. Precision is approximately95.65%, with one false positive and33 false negatives.
A card that reports only precision would hide a material limitation. The paired customer-bootstrap interval for the log-loss difference also crosses zero, so the observed improvement should not be described as decisively established by that analysis.
Record the population and time setting: the March test contains later snapshots for the same forty identities represented in training. Separate January group validation answers a different question about held-out identities and must not be merged into one unexplained score.
Include disaggregated evidence with support counts
The individual-plan test slice has52 rows and38 positives, with recall12/38. The team-plan slice has28 rows and17 positives, with recall10/17. The missing-ticket slice has only seven rows and three positives.
Those counts qualify the percentages. The card should not turn descriptive differences into causal explanations or claim a comprehensive fairness assessment from these synthetic product-plan groups.
Make limitations actionable
The example lists weak recall, changing period prevalence, limited sample support, unproven calibration, synthetic data and absent serving/monitoring integration. Each limitation points to evidence needed before a more ambitious use claim.
“More testing needed” is less useful than “evaluate the frozen procedure on a representative later period with adequate positive support and verified historical feature availability.” Distinguish a planned improvement from a completed check.
Exercise: write a one-page card for a project you have actually run. Link the result table and reproduction instructions. Ask a reviewer to identify the target, evaluation population, weakest material result and prohibited interpretation without opening your notebook. Revise any section that leaves those questions ambiguous.
NeuraPath's Data Science course connects technical projects with clear evidence reporting. A strong model card makes limitations easy to find and supports its claims with artifacts another person can inspect.
Continue learning
This article is part of the Machine learning workflow and evaluation sequence. Use the neighbouring tasks when you need the prerequisite or the next application.
- Review the prerequisite or neighbouring task in Reproduce a model result from a clean environment.
- Continue with Decide whether a model is ready for a pilot.
Pankit Kumar has 10 years in Data Science & AI, building and shipping production systems in regulated pharma and clinical environments. He is a freelance trainer at Boston Institute of Analytics, AnalytixLabs and Scaler, and has taught this material to thousands of working professionals.
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