ce-calibrated-predict

Name: ce-calibrated-predict
Availability: InStock
Author: moffran

Generate calibrated predictions and probabilities with uncertainty bounds for machine learning models.

78|15|Updated May 1, 2023

Search Tags:#calibration #predict #machine-learning #calibrated-predict #uncertainty-intervals #predict_proba #conformal-predictive-systems

Ready to run

npx skills add https://github.com/Moffran/calibrated_explanations --skill ce-calibrated-predict

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ce-calibrated-predict
Source: https://github.com/Moffran/calibrated_explanations/tree/main/.claude/skills/ce-calibrated-predict
Command: npx skills add https://github.com/Moffran/calibrated_explanations --skill ce-calibrated-predict

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Calibrated predictions and probabilities are essential when model confidence is miscalibrated; this Skill provides calibrated outputs without explanations, enabling reliable decision-making with uncertainty bounds.

Core Features & Use Cases

Calibrated point predictions via predict and calibrated probabilities via predict_proba for classification and regression tasks.
Optional uncertainty intervals: two-sided bounds on predictions and probabilities to reflect model confidence.
Flexible usage and integration: supports threshold-based probabilities, Mondrian-group conditioning, and works with a fully fitted and calibrated WrapCalibratedExplainer.

Quick Start

Call the explainer to return calibrated predictions for your data using predict or predict_proba, with optional uncertainty intervals.

Frequently Asked Questions about ce-calibrated-predict

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

FAQPage Schema

How do I get calibrated predictions with uncertainty bounds for machine learning models?▼

Calibrated predictions with uncertainty bounds are generated by passing your data through a fully fitted WrapCalibratedExplainer, which validates inputs and returns point predictions or probabilities with optional two-sided intervals.

What is the difference between predict and predict_proba for calibrated probability outputs?▼

The predict method provides calibrated point predictions for regression or classification, while predict_proba returns calibrated probabilities for classification tasks, both supporting optional uncertainty intervals to reflect model confidence.

Can I use threshold-based probabilities and Mondrian-group conditioning with calibrated predictions?▼

Threshold-based probabilities and Mondrian-group conditioning are supported for calibrated predictions, allowing flexible usage and integration when generating calibrated probabilities for classification and regression tasks.

Do I need a fitted WrapCalibratedExplainer before generating calibrated probabilities?▼

A fully fitted and calibrated WrapCalibratedExplainer is required before generating calibrated probabilities, as the Skill validates inputs against this explainer to return reliable predict and predict_proba outputs with uncertainty intervals.

Why are calibrated probabilities necessary when my model confidence is miscalibrated?▼

Calibrated probabilities are necessary when model confidence is miscalibrated because they provide reliable outputs for decision-making, ensuring the predicted probabilities accurately reflect true likelihoods with quantifiable uncertainty bounds.

Does this calibrated prediction approach work for both classification and regression tasks?▼

Calibrated predictions work for both classification and regression tasks, returning calibrated point predictions via predict or calibrated probabilities via predict_proba, with optional uncertainty bounds to reflect model confidence.