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.
8.3
Rating
0
Installs
Machine Learning
Category
Exceptional skill for SHAP-based model interpretability. The description is comprehensive and clearly maps to specific user intents (feature importance, debugging, bias analysis, plot generation). Task knowledge is thorough with concrete code examples, decision trees for explainer selection, and complete workflows for common scenarios. Structure is well-organized with a logical progression from quick start to advanced patterns, and proper delegation of deep-dive content to reference files. The skill provides significant value by consolidating SHAP's complex API, multiple explainer types, visualization options, and production patterns into actionable guidance. A CLI agent would require many trial-and-error iterations to achieve the same workflow orchestration and explainer selection logic. Minor improvement opportunity: the structure could benefit from a more prominent workflow selection menu at the top, though current organization is already strong.
K-Dense-AI
Skill Author
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