Documentation Of Experiments And Models

Why this matters

In a real Data Scientist role, clear documentation is the backbone of trustworthy ML. It lets teammates reproduce your results, auditors verify decisions, and future-you understand what you did and why.

• Reproducibility: Run the same code, data, and settings to get the same results.
• Decision traceability: Explain why you chose Model A over Model B.
• Handover: Enable teammates to continue work without guesswork.
• Compliance and risk: Show data sources, consent, and limitations.
• Monitoring: Know what “good” looks like to spot drift and incidents.

Concept explained simply

Documentation of experiments and models is a structured way to capture what you tried, what happened, and what you shipped.

• Experiment log: A concise record per run (data snapshot, parameters, metrics, and the decision).
• Dataset factsheet (data card): What the data is, where it came from, how it was cleaned, and known issues.
• Model card: What the model does, how it was trained, how to use it responsibly, and where it breaks.
• Decision record: Why you picked a model or threshold (trade-offs, risks, stakeholders).
• Versioning and lineage: IDs for data, code, model, and environment so others can reconstruct the exact pipeline.

Mental model

Think of documentation as three tools together:

• Flight recorder: Captures every run’s settings and outcomes.
• Recipe: The exact steps to recreate results.
• User manual: How to safely use the model in the real world.

What to document (checklist)

• Dataset
  • Name, version or date range
  • Source and license/consent notes
  • Splitting strategy and leakage protections
  • Preprocessing/feature engineering steps
  • Known biases, missingness, caveats
• Experiment run
  • Objective and hypothesis
  • Random seed(s), cross-validation strategy
  • Parameters and environment (packages, hardware)
  • Primary and secondary metrics (with confidence intervals if applicable)
  • Results table and decision
• Model artifact
  • Model version/ID and checksum
  • Training data version/ID
  • Model card: intended use, limitations, ethics
• Deployment and monitoring
  • Thresholds and calibration method
  • Guardrail metrics and alert thresholds
  • Rollback plan and who to contact
• Governance and risk
  • Approval dates and reviewers
  • Change log
  • Retirement or retraining criteria

Worked examples

Experiment ID: churn_lr_2025-07-14_01
Objective: Improve recall at 90% precision.
Data: customer_events_v4 (2025-06), train/val/test by time split (70/15/15)
Leakage controls: no post-churn events in features; only t-30d window used
Seed: 42   CV: 5-fold stratified
Model: Logistic Regression
Params: C=[0.1, 0.5, 1.0], class_weight=balanced, solver=liblinear
Preprocessing: StandardScaler on numeric; one-hot on top 50 categories
Metrics (val):
  C=0.1: Prec@th=0.62: 0.90, Recall: 0.52, AUROC: 0.86
  C=0.5: Prec@th=0.60: 0.90, Recall: 0.56, AUROC: 0.87
  C=1.0: Prec@th=0.58: 0.90, Recall: 0.58, AUROC: 0.87
Decision: Choose C=1.0; best recall at required precision.
Next: Calibrate threshold on test via precision-recall sweep; draft model card.

Name: transactions_v2 (2024-01 to 2024-12)
Source: Internal payments DB; aggregated daily
License/consent: Internal use; customer ToS section 3.2
Population: Active users in regions A, B, C
Preprocessing: Drop rows with >50% NaN; impute median for amount; log-transform amount
Known issues: Region C has seasonal spikes; missing merchant_category for ~8%
Intended use: Fraud detection training
Limitations: Not representative of region D; suspect merchant_category drift after Nov

Model: fraud_xgb_v3 (XGBoost)
Intended use: Flag high-risk transactions for review
Out-of-scope: Blocking payments automatically without human review
Training data: transactions_v2 (2024-01..12); stratified by user_id
Metrics (test): AUROC=0.941; PR-AUC=0.312
Calibration: Platt scaling on validation set
Fairness: Evaluated FPR parity across regions; region C FPR +1.8pp vs A (monitor)
Limitations: Underperforms on new merchants; sensitive to amount outliers
Ethical considerations: False positives increase manual workload; human-in-the-loop required
Owner: Risk ML Team; Contact: risk-ml@company
Versioning: Code v1.8.2; Data v2; Model checksum a1b2c3

Minimal templates you can copy

# Experiment: <id> (date, time, author)
Objective:
Data: <name/version/date range> | Split: <method>
Leakage controls:
Seed(s): <...>  CV: <...>
Model:
Params:
Preprocessing:
Metrics (val/test):
Results table:
Decision:
Next action:

Name:
Source:
License/consent:
Population:
Collection period:
Preprocessing/feature engineering:
Quality notes (missingness, bias, drift risks):
Intended use and out-of-scope:
Limitations:

Model name/version:
Intended use / Out-of-scope:
Training data summary:
Training procedure and environment:
Metrics (with definitions):
Calibration/threshold(s):
Fairness/robustness checks:
Known limitations and failure modes:
Ethical considerations and mitigations:
Owner/contact and review approvals:
Change log:

Workflow: from idea to shipped model

1. Define objective and hypothesis (which metric will move and why).
2. Prepare dataset factsheet and snapshot the data version.
3. Plan experiments (seeds, CV, parameter ranges).
4. Run experiments and fill an experiment log per run.
5. Compare runs and create a decision record (trade-offs, risks).
6. Write the model card draft and get review.
7. Deploy with thresholds, guardrails, and monitoring plan documented.
8. Update documents with post-deployment findings and changes.

Common mistakes and how to self-check

• Only logging the best run. Fix: Log every run and the selection criteria.
• Forgetting seeds or package versions. Fix: Capture seed(s), Python/OS, and key library versions.
• Unclear data splits. Fix: Describe split method and leakage protections.
• Ambiguous metrics. Fix: Name metrics precisely (e.g., PR-AUC), include thresholds and intervals if used.
• No limitations section. Fix: Always state where the model may fail.

Exercises

Note: The Quick Test is available to everyone; only logged-in users get saved progress. Look for the section titled "Documentation Of Experiments And Models — Quick Test" below.

• [ ] I included seeds, versions, and data snapshot IDs.
• [ ] Metrics and thresholds are defined and comparable.
• [ ] Decision criteria and trade-offs are explicit.
• [ ] Limitations, risks, and monitoring plan are present.

Mini challenge

Your best offline model has slightly worse recall but better precision than the baseline. Stakeholders care most about reducing false positives in a human-review workflow. Write 3 sentences for a decision record justifying which model to ship and what to monitor post-deployment. Keep it crisp and measurable.

Who this is for

• Data Scientists and ML Engineers who run experiments and ship models.
• Analysts transitioning to ML who need reproducible workflows.
• Students building portfolios that look professional.

Prerequisites

• Basic ML training workflow (train/validate/test, metrics).
• Familiarity with versioning (git) and environments (conda/venv or containers).
• Comfort with Markdown or simple text docs.

Learning path

1. Start using the experiment log template for every run this week.
2. Create a dataset factsheet for your main dataset.
3. Draft a model card for your current best model and request peer review.
4. Add a decision record after each comparison or A/B test.
5. Integrate documentation checkpoints into your PR process.

Practical projects

• Reproduce a public notebook’s results and write your own experiment log narrating differences.
• Turn a classroom model into a production-ready artifact with a full model card and monitoring plan.
• Build a small internal "model registry" folder structure with logs, cards, and factsheets for two different models.

Next steps

• Adopt a consistent naming and versioning scheme across data, code, and models.
• Automate parts of logging (auto-capture env, params, and metrics) while keeping human-written decisions.
• Run the Quick Test below to cement your understanding and find gaps.