SHAP (SHapley Additive exPlanations)
This skill provides comprehensive coverage of SHAP for model interpretability across all use cases and model types.
LLM Evaluation
Evaluated by: xiaomi/mimo-v2-flash:free
Last evaluated: March 29, 2026
Prompt Preview
---
name: shap
description: 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 mod...Full prompt length: 18405 characters
Tools & Technologies
- go
- python