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shap

Generates SHAP visualizations and analyses for model interpretability.

1|Updated Mar 12, 2026
Search Tags:#debugging#machine learning#model interpretability#feature importance#shap#explainable ai
One-click install
npx skills add https://github.com/yf8578/clawomics --skill shap-yf8578
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: shap
Source: https://github.com/yf8578/clawomics/tree/main/skills/shap
Command: npx skills add https://github.com/yf8578/clawomics --skill shap-yf8578

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires shap, matplotlib, xgboost, lightgbm, tensorflow, torch, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides deep insights into how machine learning models make predictions, helping users understand feature importance, debug models, and ensure fairness.

Core Features & Use Cases

  • Model Interpretability: Explain predictions of any ML model (tree-based, deep learning, linear, black-box).
  • Feature Importance: Quantify the impact of each feature on model outcomes.
  • Debugging & Validation: Identify biases, data leakage, and unexpected model behavior.
  • Use Case: A data scientist has trained a complex XGBoost model for credit risk assessment. They use this Skill to explain why a specific applicant was denied, identifying the key features (e.g., credit score, debt-to-income ratio) that contributed most to the negative prediction.

Quick Start

Use the shap skill to generate a beeswarm plot for the model explanations.

Frequently Asked Questions about shap

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I explain machine learning model predictions and understand feature importance?▼

Model interpretability is achieved by calculating SHAP values to quantify the impact of each feature on model outcomes. This provides detailed, instance-level and global insights, helping you understand exactly how specific predictions are made.

Does SHAP work with deep learning frameworks like TensorFlow and PyTorch?▼

Yes, SHAP works with TensorFlow and PyTorch to provide explainable AI for deep learning models. It also supports tree-based models like XGBoost and LightGBM, ensuring comprehensive model interpretability across diverse architectures.

How do I debug machine learning models and identify data bias?▼

Debugging models and identifying bias is done by analyzing SHAP values to isolate unexpected model behavior and data leakage. This process reveals the underlying feature contributions driving predictions, ensuring model fairness and validation.

What is the best way to visualize feature contributions for a complex XGBoost model?▼

The best way to visualize feature contributions for an XGBoost model is by generating a SHAP beeswarm plot. This visualization maps the magnitude and direction of feature impacts across all predictions, delivering clear global model insights.

Can I generate instance-level explanations for credit risk assessment predictions?▼

Yes, you can generate instance-level explanations for credit risk predictions using SHAP. This allows you to explain why a specific applicant was denied by isolating the exact features, such as credit score, that contributed to the negative prediction.

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