Feature Attribution
by AaaS · free · Last verified 2026-03-17
This skill involves computing and communicating which input features most influenced a model's prediction. It leverages methods like SHAP, LIME, and Integrated Gradients for tabular, text, and image data. The core focus is on generating local and global explanations and presenting them visually for both technical and non-technical audiences.
https://aaas.blog/skill/feature-attribution ↗B
B—Above Average
Adoption: B+Quality: AFreshness: ACitations: BEngagement: F
Specifications
- License
- MIT
- Pricing
- free
- Capabilities
- Compute SHAP values for global and local feature importance, Generate LIME explanations for individual predictions, Apply Integrated Gradients to deep learning models, Visualize attention maps in transformer-based models, Create attribution plots and summaries for stakeholder reports, Explain model predictions for tabular, text, and image data, Debug models by identifying influential but irrelevant features, Assess model fairness by comparing feature attributions across demographic groups
- Integrations
- [object Object], [object Object], [object Object], [object Object]
- Use Cases
- [object Object], [object Object], [object Object], [object Object], [object Object]
- API Available
- No
- Difficulty
- intermediate
- Prerequisites
- prompt-engineering
- Supported Agents
- compliance-agent
- Tags
- xai, explainable-ai, interpretability, shap, lime, integrated-gradients, attention-attribution, model-debugging, responsible-ai, feature-importance, model-transparency
- Added
- 2026-03-17
- Completeness
- 1%
Index Score
62.6Adoption
72
Quality
84
Freshness
80
Citations
68
Engagement
0