shap-explainer

What problem does it solve?

This Skill addresses the need for transparency and interpretability in machine learning models by explaining their predictions using SHAP values.

Core Features & Use Cases

• Local Feature Importance: Understand which features influenced a specific prediction.
• Global Feature Importance: Identify the most influential features across the entire dataset.
• Dependency Plots: Visualize the relationship between a feature and the model's output.
• Use Case: A financial institution uses a model to predict loan default risk. This Skill can explain why a particular applicant was flagged as high-risk by highlighting the key factors (e.g., credit score, debt-to-income ratio) that contributed to that decision, aiding in regulatory compliance and customer communication.

Quick Start

Use the shap-explainer skill to explain the prediction for the provided data point.