home / skills / amnadtaowsoam / cerebraskills / confidence-scoring

confidence-scoring skill

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This skill provides guidance on confidence scoring and calibration within model explainability, helping you interpret predictions and communicate uncertainty

npx playbooks add skill amnadtaowsoam/cerebraskills --skill confidence-scoring

Review the files below or copy the command above to add this skill to your agents.

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SKILL.md

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---
name: Confidence Scoring
description: See the main Model Explainability skill for comprehensive coverage of confidence scoring and calibration.
---

# Confidence Scoring

This skill is covered in detail in the main **Model Explainability** skill.

Please refer to: `44-ai-governance/model-explainability/SKILL.md`

That skill covers:
- SHAP and LIME for feature importance
- Confidence scoring and interpretation
- Calibration techniques
- Explainability for different model types
- LLM-specific explainability
- Presenting explanations to users
- Tools (SHAP, LIME, InterpretML, Captum)
- Real-world explainability examples

For confidence-specific topics, also see:
- Confidence thresholds in `44-ai-governance/human-approval-flows`
- Model risk management in `44-ai-governance/model-risk-management`

---

## Related Skills
* `44-ai-governance/model-explainability` (Main skill)
* `44-ai-governance/human-approval-flows`
* `44-ai-governance/model-risk-management`

Overview

This skill provides practical guidance on confidence scoring and calibration for machine learning models, as part of broader model explainability practices. It explains how to generate, interpret, and calibrate confidence estimates so you can make safer, more transparent decisions from model outputs.

How this skill works

The skill covers methods to compute confidence scores (probabilistic outputs, softmax, predictive distributions), evaluate their reliability using calibration metrics (reliability diagrams, Brier score, expected calibration error), and apply post-hoc calibration techniques (Platt scaling, isotonic regression, temperature scaling). It also outlines how to present confidence to users and integrate thresholds into decision or human-review flows.

When to use it

When model probabilities will influence risky or high-stakes decisions
Before deploying models that produce probabilistic outputs in production
When users need understandable indicators of model uncertainty
To set safe automated decision thresholds or human review gates
When evaluating or comparing models beyond raw accuracy

Best practices

Report calibrated probabilities instead of raw model scores for decisioning
Use a held-out validation set for calibration fitting and separate test data for evaluation
Visualize calibration with reliability diagrams and track calibration drift over time
Combine confidence scores with coverage policies—avoid over-reliance on single metrics
Surface confidence to end users with clear explanations and recommended actions

Example use cases

Triage system that routes low-confidence predictions to human reviewers
Healthcare risk models where calibrated probabilities guide treatment prioritization
Fraud detection systems that escalate transactions below a confidence threshold
Model comparison dashboards that include Brier score and calibration curves
LLM output filtering that uses confidence estimates to flag hallucinations

FAQ

How is confidence different from accuracy?

Accuracy measures correctness on a dataset; confidence reflects the model's estimated probability for a specific prediction. A model can be accurate on average but poorly calibrated, meaning its probabilities are not reliable.

When should I recalibrate a model?

Recalibrate when you observe calibration drift due to data distribution shifts, after retraining on new data sources, or if calibration metrics degrade in monitoring.