AlterLab SHAP
Skill Verified ActivePart of the AlterLab Academic Skills suite. Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.
To empower users to understand and explain their machine learning model predictions using the SHAP framework, enabling better debugging, fairness analysis, and model deployment.
Features
- Compute SHAP values for diverse model types (tree, deep learning, linear, black-box)
- Generate a wide range of SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap)
- Provide detailed workflows for model explanation, debugging, feature engineering, and fairness analysis
- Offer comprehensive reference documentation for explainers, plots, theory, and advanced techniques
- Guidance on production deployment and performance optimization
Use Cases
- Explaining individual model predictions to stakeholders
- Debugging why a model made a specific incorrect prediction
- Identifying and quantifying feature importance across a dataset
- Analyzing model bias and fairness across different demographic groups
- Understanding feature interactions and nonlinear relationships
Non-Goals
- Training machine learning models
- Performing hyperparameter tuning
- Deploying models directly to production environments (guidance provided, not direct deployment)
- Replacing the need for domain expertise in interpreting results
Workflow
- Select the appropriate SHAP explainer based on model type.
- Compute SHAP values for the model's predictions using background data.
- Visualize results using plots like waterfall, beeswarm, bar, or scatter plots.
- Interpret feature contributions to understand global importance and individual predictions.
- Debug model behavior, analyze fairness, or engineer features based on insights.
- Integrate explanations into production systems or workflows.
Practices
- Model Interpretability
- Explainable AI
- Machine Learning Workflow
Prerequisites
- Python 3.7+
- numpy
- pandas
- scikit-learn
- matplotlib
- shap
- xgboost, lightgbm, tensorflow, torch (depending on model type)
Execution
- info:Pinned dependenciesDependencies are listed, and installation instructions suggest standard package managers, but explicit lockfiles for pinning are not evident.
Installation
npx skills add AlterLab-IEU/AlterLab-Academic-SkillsRuns the Vercel skills CLI (skills.sh) via npx — needs Node.js locally and at least one installed skills-compatible agent (Claude Code, Cursor, Codex, …). Assumes the repo follows the agentskills.io format.
Quality Score
VerifiedTrust Signals
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