{"enrichment":{"faq":[{"a":"ML Model Explanation provides multiple techniques to understand why your model made specific predictions. Use SHAP values for feature attribution, LIME for local interpretable approximations, partial dependence plots for global patterns, and attention visualization for neural networks. These methods reveal both individual prediction drivers and broader model behavior, enabling you to justify decisions to stakeholders and meet regulatory requirements.","q":"How to explain machine learning model predictions?"},{"a":"ML Model Explanation covers SHAP values as a game-theoretic approach to feature attribution. SHAP calculates each feature's contribution to moving a prediction from the model's base value to the actual prediction. This technique provides consistent, locally accurate explanations and helps identify which features most influence individual predictions, supporting both model debugging and compliance documentation.","q":"What are SHAP values and feature attribution?"},{"a":"ML Model Explanation includes LIME (Local Interpretable Model-agnostic Explanations) for understanding individual predictions. LIME approximates your black-box model locally using an interpretable surrogate model around a specific prediction. This technique works across any model type and reveals which features drove that particular decision, complementing global techniques like partial dependence plots.","q":"How does LIME provide local interpretable explanations?"},{"a":"ML Model Explanation teaches multiple feature importance approaches: permutation importance, impurity-based importance, SHAP values, and partial dependence analysis. Each method reveals different aspects\u2014permutation importance shows real-world impact, impurity-based methods reflect training dynamics, and SHAP provides theoretically grounded attribution. Choose based on your interpretability needs and model type.","q":"What feature importance analysis methods does ML Model Explanation cover?"},{"a":"ML Model Explanation enables regulatory compliance through transparent, documented model reasoning. Generate feature contribution explanations for individual predictions to satisfy GDPR's right to explanation. Audit decision patterns across demographic groups using global and local explainability techniques. Build audit trails showing which features drove lending or credit decisions, supporting fair lending documentation requirements.","q":"How can ML Model Explanation ensure GDPR and fair lending compliance?"},{"a":"ML Model Explanation covers both explanation scopes. Global methods (partial dependence, feature importance) reveal overall model patterns and feature relationships across all predictions. Local methods (SHAP, LIME) explain individual predictions by showing which features drove that specific decision. Use global explanations for model understanding and debugging; use local explanations for individual prediction justification and compliance.","q":"What's the difference between local vs global model explanations?"}],"shadow_tags":["model-transparency","prediction-debugging","feature-attribution","interpretable-ai","decision-justification","compliance-ready","trust-building","black-box-analysis","model-diagnostics","explainability-framework"],"summary_rewrite":"Unlock the reasoning behind your model's predictions using multiple explainability techniques. This skill covers feature importance, SHAP values, LIME local approximations, partial dependence plots, and attention visualization to reveal both global patterns and individual prediction drivers. Build trust and compliance through transparent, interpretable machine learning."},"files":[{"bytes":12901,"path":"skills/ml-model-explanation/SKILL.md","sha256":"ceadfaf8c342b614a670597bb962d85b6a22e1053dca244ea6d1e8181587c265","url":"https://skillfed.io/files/aj-geddes/useful-ai-prompts/ml-model-explanation/11f086d0/SKILL.md"}],"id":"aj-geddes/useful-ai-prompts/ml-model-explanation","links":{"html":"https://skillfed.io/aj-geddes/useful-ai-prompts/ml-model-explanation","md":"https://skillfed.io/aj-geddes/useful-ai-prompts/ml-model-explanation.md","repo":"https://github.com/aj-geddes/useful-ai-prompts"},"meta":{"agents_supported":[],"first_seen":"2026-07-28","forks":45,"language":"Shell","last_updated":"2026-03-04","license":"MIT","name":"ML Model Explanation","publisher":"aj-geddes","stars":299},"relations":{"similar":[{"id":"datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/cost-prediction"},{"id":"jaechang-hits/SciAgent-Skills/shap-model-explainability"},{"id":"eyadsibai/ltk/shap"},{"id":"tondevrel/scientific-agent-skills/xgboost-lightgbm"},{"id":"K-Dense-AI/scientific-agent-skills/shap"},{"id":"aj-geddes/useful-ai-prompts/sentiment-analysis"},{"id":"personamanagmentlayer/pcl/ml-expert"},{"id":"synthetic-sciences/openscience/shap"},{"id":"foryourhealth111-pixel/Vibe-Skills/shap"},{"id":"LeonChaoX/qinyan-academic-skills/shap"}]},"slug":{"owner":"aj-geddes","repo":"useful-ai-prompts","skill":"ml-model-explanation"},"version":"11f086d0"}