Term
Feature Attribution
Definition
Feature Attribution gives scores to different parts of the data that an AI system uses to figure out an answer. These scores show which parts are most important in reaching a decision.
Where you’ll find it
In AI tools and platforms, feature attribution typically appears in the analysis or model explanation sections. These are found on dashboards or in the outputs of machine learning models, especially those focused on transparency.
Common use cases
- To understand which data inputs impact the AI's decisions the most.
- To improve model design by identifying which inputs can be modified or optimized.
- To explain AI decisions in a way that users can trust and understand.
Things to watch out for
- Feature Attribution scores do not explain why a feature is important, only that it is important.
- Sometimes, it can oversimplify complex interactions of inputs, leading to misinterpretation.
- It's crucial to pair feature attribution with other analysis tools for full model comprehension.
Related terms
- Machine Learning
- Model Interpretability
- SHAP
- LIME
- Integrated Gradients
Pixelhaze Tip: Always cross-verify the importance scores from Feature Attribution with real-world data or additional analysis tools. This helps prevent reliance on potentially misleading insights and ensures a more complete understanding of your AI model's behavior.