Ml Model Evaluation

Ml Model Evaluation

Overview

Use this skill to evaluate models with decision-grade evidence across aggregate and high-risk segments.

Scope Boundaries

• Use this skill when the task matches the trigger condition described in description.
• Do not use this skill when the primary task falls outside this skill's domain.

Shared References

• Threshold and segmentation rules:
  • references/threshold-and-segmentation-rules.md

Templates And Assets

• Evaluation report template:
  • assets/evaluation-report-template.md

Inputs To Gather

• Dataset splits and baseline/candidate definitions.
• Business cost trade-offs for false positives/negatives.
• Segment definitions for fairness/risk-critical cohorts.
• Acceptance thresholds and calibration requirements.

Deliverables

• Evaluation report with thresholds and decision.
• Segment-level failure analysis.
• Acceptance/rejection rationale and follow-ups.

Workflow

1. Build evaluation report in assets/evaluation-report-template.md.
2. Apply threshold/segment policy via references/threshold-and-segmentation-rules.md.
3. Validate calibration and error concentration risks.
4. Compare baseline vs candidate under same conditions.
5. Publish release recommendation and unresolved risks.

Quality Standard

• Thresholds are tied to business risk trade-offs.
• Critical segments are explicitly evaluated.
• Decision rationale is traceable to evidence.

Failure Conditions

• Stop when evaluation omits high-risk segments.
• Stop when acceptance thresholds are undefined.
• Escalate when model risk is unacceptable for rollout.