Model Interpretability Tool
Software tools that help understand how ML models make predictions, including feature importance, attention visualization, counterfactual explanations, and decision path analysis.
Why It Matters
Interpretability tools bridge the gap between black-box models and stakeholder trust. They answer 'why did the model make this prediction?'
Example
Using SHAP waterfall plots to show a loan officer exactly which factors drove a specific approval or denial, enabling them to explain the decision to the customer.
Think of it like...
Like a dashboard in a car — you do not need to understand the engine's internals, but you need gauges that tell you what is happening.
Related Terms
Explainability
The ability to understand and articulate how an AI model reaches its decisions or predictions. Explainable AI (XAI) makes the decision-making process transparent and comprehensible to humans.
SHAP
SHapley Additive exPlanations — a method based on game theory that explains individual predictions by calculating each feature's contribution to the prediction. SHAP values are additive and consistent.
LIME
Local Interpretable Model-agnostic Explanations — a technique that explains individual predictions by approximating the complex model locally with a simple, interpretable model.
Interpretability
The degree to which a human can understand the internal mechanisms and reasoning process of a machine learning model. More interpretable models allow deeper inspection of how they work.