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ce-classification

Calibrate binary and multiclass classification predictions with Venn-Abers calibration.

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

CE classification provides calibrated probability bounds using Venn-Abers calibration for binary and multiclass semantics to produce reliable explanations.

Core Features & Use Cases

  • Explains the predicted class by returning calibrated probabilities and class-specific explanations.
  • Supports explaining all classes when multi_labels_enabled is true for contrastive analysis.
  • Suitable for pipelines requiring robust uncertainty estimates and interpretable decisions.

Quick Start

Fit your classifier, calibrate its outputs, and generate a fact-based explanation for the model’s predicted class.

Frequently Asked Questions about ce-classification

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

FAQPage Schema
How do I calibrate classification predictions for reliable uncertainty estimates?▼

Calibrate classification predictions by applying Venn-Abers calibration to generate calibrated probabilities and bounded intervals, providing reliable uncertainty estimates for decision-making.

What is Venn-Abers calibration and how does it explain classification outcomes?▼

Venn-Abers calibration produces calibrated probability bounds that enforce low <= p <= high invariants, providing fact-based explanations for binary and multiclass classification predictions.

Can I generate contrastive explanations across all classes in multiclass classification?▼

Yes, enabling multi_labels_enabled allows multiclass classification models to explain all classes, returning calibrated probabilities and class-specific explanations for contrastive analysis.

Does Venn-Abers calibration support both binary and multiclass semantics?▼

Venn-Abers calibration supports binary semantics relative to the positive class and multiclass semantics with an option to explain all classes, maintaining invariants such as low <= p <= high.

How do I debug model predictions using calibrated probability intervals?▼

Debug model predictions by fitting your classifier, calibrating its outputs with Venn-Abers, and generating fact-based explanations for the predicted class to analyze uncertainty and decision reliability.

When should I not use Venn-Abers calibration for classification explanation?▼

Venn-Abers calibration is not suitable for classification pipelines that do not require robust uncertainty estimates, calibrated probability bounds, or interpretable decisions for model debugging.

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