evaluate-model-calibration

What problem does it solve?

This Skill addresses the critical issue of AI model calibration, ensuring that predicted probabilities accurately reflect the likelihood of actual outcomes, which is essential for building clinical trust.

Core Features & Use Cases

• Calibration Assessment: Learn to differentiate calibration from discrimination and understand its importance.
• Visualization: Create reliability diagrams (calibration plots) to visually assess model calibration.
• Metric Calculation: Compute Expected Calibration Error (ECE) and Brier Score for quantitative evaluation.
• Clinical Interpretation: Understand the real-world impact of miscalibration on clinical decision-making.
• Recalibration Strategies: Explore methods to improve model calibration when necessary.
• Use Case: A clinician needs to decide whether to order a CT scan based on an AI's TBI risk prediction. This Skill helps ensure the AI's "90% risk" prediction truly means there's a 90% chance of TBI, preventing unnecessary scans or missed diagnoses.

Quick Start

Use the evaluate-model-calibration skill to generate a reliability diagram and calculate the ECE for the provided model predictions and true outcomes.