ml-model-explainer

What problem does it solve?

This Skill helps users understand why a machine learning model makes specific predictions by breaking down complex decisions into understandable components.

Core Features & Use Cases

• Explain Individual Predictions: Use SHAP values to see which features contributed most to a single outcome.
• Global Feature Importance: Understand which features are generally most influential across all predictions.
• Visualize Decision Paths: Trace the logic of tree-based models to see how a prediction was reached.
• Use Case: A data scientist can use this Skill to explain to a stakeholder why a loan application was denied, highlighting the key factors that led to the decision.

Quick Start

Use the ml-model-explainer skill to explain the prediction for the first data point in test.csv using model.pkl and save the output to the explanations directory.