ML Model Explanation
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.
ML Model Explanation helps you understand why your machine learning model made a specific prediction using SHAP, LIME, and feature importance techniques.
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Install
aj-geddes/useful-ai-prompts/ml-model-explanation · repository language: Shell
git clone https://github.com/aj-geddes/useful-ai-prompts
cp -r useful-ai-prompts/skills/ml-model-explanation ~/.claude/skills/ml-model-explanationnpx skillfed install aj-geddes/useful-ai-prompts/ml-model-explanationFrequently asked questions
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How to explain machine learning model predictions?
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.
What are SHAP values and feature attribution?
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.
How does LIME provide local interpretable explanations?
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.
What feature importance analysis methods does ML Model Explanation cover?
ML Model Explanation teaches multiple feature importance approaches: permutation importance, impurity-based importance, SHAP values, and partial dependence analysis. Each method reveals different aspects—permutation 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.
How can ML Model Explanation ensure GDPR and fair lending compliance?
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.
What's the difference between local vs global model explanations?
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.
SKILL.md
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ML Model Explanation
Model explainability makes machine learning decisions transparent and interpretable, enabling trust, compliance, debugging, and actionable insights from predictions.
Explanation Techniques
- Feature Importance: Global feature contribution to predictions
- SHAP Values: Game theory-based feature attribution
- LIME: Local linear approximations for individual predictions
- Partial Dependence Plots: Feature relationship with predictions
- Attention Maps: Visualization of model focus areas
- Surrogate Models: Simpler interpretable approximations
Explainability Types
- Global: Overall model behavior and patterns
- Local: Explanation for individual predictions
- Feature-Level: Which features matter most
- Model-Level: How different components interact
Python Implementation
```python import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from sklearn.datasets import make_classification from
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