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ce-factual-explain

Generate factual Calibrated Explanations for model predictions with explain_factual workflows.

78|15|Updated May 1, 2023
Search Tags:#calibration#uncertainty#multiclass#factual-explanations#guarded-semantics#explanation-api
One-click install
npx skills add https://github.com/Moffran/calibrated_explanations --skill ce-factual-explain
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ce-factual-explain
Source: https://github.com/Moffran/calibrated_explanations/tree/main/.claude/skills/ce-factual-explain
Command: npx skills add https://github.com/Moffran/calibrated_explanations --skill ce-factual-explain

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Generate factual Calibrated Explanations (CE) for model predictions.

Core Features & Use Cases

  • Generate factual calibrated explanations (CE explanations) for individuals or multiclass outputs.
  • Choose between standard explain_factual pipelines and guarded explain_guarded_factual pipelines to handle unknown input distributions and production endpoints.
  • Reference ADR-032 guarded semantics for safety.

Quick Start

Call explain_factual on a query to generate a factual CE explanation with calibrated uncertainty.

Frequently Asked Questions about ce-factual-explain

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate factual calibrated explanations for model predictions?▼

Standard factual CE explanations apply the explain_factual workflow, while guarded factual CE explanations use explain_guarded_factual to handle unknown input distributions and production endpoints safely via guarded audit semantics.

How do I handle unknown input distributions with calibrated explanations?▼

Yes, factual calibrated explanations support multiclass outputs by enforcing the CE API surface with interval invariants, allowing you to generate calibrated uncertainty explanations across multiple classification categories.

What is the difference between standard and guarded factual CE explanations?▼

Standard factual CE explanations apply the explain_factual workflow, while guarded factual CE explanations use explain_guarded_factual to handle unknown input distributions and production endpoints safely via guarded audit semantics.

Can I use factual calibrated explanations for multiclass classification outputs?▼

Yes, factual calibrated explanations support multiclass outputs by enforcing the CE API surface with interval invariants, allowing you to generate calibrated uncertainty explanations across multiple classification categories.

How do I convert calibrated explanations into narrative format?▼

Use the to_narrative feature to transform factual CE explanations into readable text, and use list_rules to extract the calibrated decision rules governing the model's factual prediction intervals.

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