model-evaluation-reporting

Purpose

Raw accuracy metrics are not enough. Evaluation must reflect the actual business impact and failure modes of the model; this standard turns metrics into a report a stakeholder can act on.

When to Use

• Producing an evaluation report for a trained model
• Reviewing a report that quotes a single headline metric
• A go/no-go decision depends on understanding error costs

Inputs

• Model predictions on a held-out set
• Business context: cost of errors, key user/customer segments

Workflow

1. Beyond accuracy: include the confusion matrix; report Precision, Recall, and F1; explain the cost of a False Positive vs a False Negative in the business context.
2. Slice analysis: report performance on key segments. A model can be 95% accurate overall but only 40% accurate on new users.
3. Calibration: if the model outputs probabilities, verify they are calibrated — a prediction of 0.8 should come true about 80% of the time.
4. Format as a Markdown report that a non-technical stakeholder can read, highlighting trade-offs and worst-case scenarios.

Output

• A Markdown evaluation report: headline metrics, confusion matrix, per-segment table, calibration assessment, and explicit trade-offs/worst cases

Verification

• Confusion matrix plus Precision/Recall/F1 present (not accuracy alone)
• False Positive vs False Negative costs stated in business terms
• Key segments sliced and reported
• Calibration assessed whenever probabilities are output
• Report readable by a non-technical stakeholder

Failure Modes

• Headline-metric reporting — one aggregate number hides segment failures; always include slices.
• Cost-free framing — treating FP and FN as equivalent when their business impact differs by orders of magnitude.
• Uncalibrated probabilities read as confidence — stakeholders will read 0.8 as "80% sure"; verify calibration before they do.